Written by Asaf Somekh, CEO & Founder at iguazio

We are witnessing a technological revolution that is dramatically changing the way we live and work. The speed at which technological breakthroughs are occurring has no precedent in previous periods of transformation. This revolution is disrupting almost every industry in every country.

Most of the technological revolutions now taking place or about to take place are based on AI. Adoption of AI in businesses across the US, Europe and China has risen sharply over the past year, to 34 percent. AI technology uses algorithms to analyze large data sets, detect patterns, extract insights, and make decisions accordingly. Israel is widely celebrated as an AI powerhouse, despite its population size.

AI technologies make it possible to use the massive amounts of accumulated data and make use of them. The growing market around AutoML solutions has made data science accessible to a larger segment of organizations. However, according to industry analysts an estimated 85% of data science projects, which have shown great promise in the lab, never make it to production. This is due to the challenges of transforming an AI model, which is functional and shows great promise in lab conditions, to a fully operational AI application that can deliver business impact at scale and in real business environments.

The potential use cases for data science are truly exciting. But the elaborate challenges of operationalizing machine learning can--and often does--impede companies from bringing innovative solutions to market. Software development has by now become a repeatable efficient practice, but for the AI industry, the complexities involved in machine learning applications means there is still a lack of standards and widespread best practices.

This is changing. MLOps (Machine Learning Operations) is an emerging discipline that echoes DevOps practices for machine and deep learning. MLOps decreases time and effort by automating the tasks involved with deploying, monitoring and managing AI applications in production. As the MLOps field evolves, new technologies like feature stores are emerging to break down silos between data scientists, data engineers and DevOps practitioners, by allowing everyone on the team to build, share, reuse, analyze and monitor features in production. This unified approach to feature engineering accelerates the path from research to production and enables companies to develop, deploy and manage AI at scale with ease. As companies across industries weave AI/ML applications into their processes, IT leaders must invest in MLOps to drive real business impact.

The ARC Innovation Center at Sheba Medical Center, ranked one of the top ten hospitals worldwide by Newsweek, is a standout example of how AI can be operationalized to dramatically improve healthcare. Sheba Medical Center is the largest hospital in Israel and the MEA region, and possesses one of the worlds largest reservoirs of health data. ARC recently launched a new project that brings urgent real-time predictions to the ICU floor. Harnessing data from various sources, such as real-time vital signs, x-rays and historic patient records, they use advanced machine learning algorithms to optimize patient care, predict COVID-19 patient deterioration and even control the flow of cars to parking spaces . Real-time dashboards surface alerts for doctors and prioritize patient intake, so the medical center can respond quickly and dramatically improve outcomes for all involved.

The massive strain placed on companies and health organizations by COVID-19 has only emphasized what we knew before: it is absolutely vital for businesses who want to survive the current situation to create a competitive advantage by bringing AI innovations to market quickly. Companies must adapt to a rapidly shifting environment by focusing on developing and deploying AI more efficiently, without the excessive costs and lengthy timeframes that might have seemed reasonable just a year ago. And now more than ever, monitoring AI applications for concept drift is critical, as human behavior changes dramatically from week to week during these unpredictable times, leaving AI models unusable due to a change in the very things they were built to predict.

With the new Israeli government setting a goal to increase the percentage of high tech employees from 10% to 15% of the overall workforce, AI technologies will be a critical growth engine for the Israeli economy. Israels past success establishing thought leadership and market dominance in the cybersecurity market bodes well for its ability to overcome the current obstacles it faces on the path to global AI leadership. MLOps will be a big facilitator in this path, enabling more and more companies to see real business value from their AI endeavors, in a short timeframe and with a lean team.

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MLOps: The Latest Shift in the AI Market in Israel - Geektime

In March 2020, as the spread of Covid-19 sent shockwaves around the nation, integrative biologist Lauren Ancel Meyers gave a virtual presentation to the press about her findings. In talking about how the disease could devastate local hospitals, she pointed to a graph where the steepest red curve on it was labeled: no social distancing. Hospitals in the Austin, Texas, area would be overwhelmed, she explained, if residents didnt reduce their interactions outside their household by 90 percent.

Meyers, who models diseases to understand how they spread and what strategies mitigate them, had been nervous about appearing in a public event and even declined the invitation at first. Her team at the University of Texas at Austin had just joined the city of Austins task force on Covid and didnt know how, exactly, their models of Covid would be used. Moreover, because of the rapidly evolving emergency, her findings hadnt been vetted in the usual way.

We were confident in our analyses but had never gone public with model projections that had not been through substantial internal validation and peer review, she writes in an e-mail. Ultimately, she decided the public needed clear communication about the science behind the new stay-at-home order in and around Austin.

The Covid-19 pandemic sparked a new era of disease modeling, one in which graphs once relegated to the pages of scientific journals graced the front pages of major news websites on a daily basis. Data scientists like Meyers were thrust into the public limelightlike meteorologists forecasting hurricanes for the first time on live television. They knew expectations were high, but that they could not perfectly predict the future. All they could do was use math and data as guides to guess at what the next day would bring.

As more of the United States population becomes fully vaccinated and the nation approaches a sense of pre-pandemic normal, disease modelers have the opportunity to look back on the last year-and-a-half in terms of what went well and what didnt. With so much unknown at the outsetsuch as how likely is an individual to transmit Covid under different circumstances, and how fatal is it in different age groupsits no surprise that forecasts sometimes missed the mark, particularly in mid-2020. Models improved as more data became available on not just disease spread and mortality, but also on how human behavior sometimes differed from official public health mandates.

Modelers have had to play whack-a-mole with challenges they didnt originally anticipate. Data scientists didnt factor in that some individuals would misinterpret or outright ignore the advice of public health authorities, or that different localities would make varying decisions regarding social-distancing, mask-wearing and other mitigation strategies. These ever-changing variables, as well as underreported data on infections, hospitalizations and deaths, led models to miscalculate certain trends.

Basically, Covid threw everything at us at once, and the modeling has required extensive efforts unlike other diseases, writes Ali Mokdad, professor at the Institute for Health Metrics and Evaluation, IHME, at the University of Washington, in an e-mail.

Still, Meyers considers this a golden age in terms of technological innovation for disease modeling. While no one invented a new branch of math to track Covid, disease models have become more complex and adaptable to a multitude of changing circumstances. And as the quality and amount of data researchers could access improved, so did their models.

A model uses math to describe a system based on a set of assumptions and data. The less information available about a situation so far, the worse the model will be at both describing the present moment and predicting what will happen tomorrow.

So in early 2020, data scientists never expected to exactly divine the number of Covid cases and deaths on any given day. But they aimed to have some framework to help communities, whether on a local or national level, prepare and respond to the situation as well as they could.

Models are like guardrails to give some sense of what the future may hold, says Jeffrey Shaman, director of the Climate and Health Program at the Columbia University Mailman School of Public Health.

You need to sort of suss out what might be coming your way, given these assumptions as to how human society will behave, he says. And you have to change those assumptions, so that you can say what it may or may not do.

The Covid crisis also led to new collaborations between data scientists and decision-makers, leading to models oriented towards actionable solutions. When researchers partnered with public health professionals and other local stakeholders, they could tailor their forecasts toward specific community concerns and needs.

Meyers team has been an integral part of the Austin areas Covid plans, meeting frequently with local officials to discuss the latest data, outlook and appropriate responses. The municipal task force brings together researchers with the mayor, the county judge, public health authorities, CEOs of major hospitals and the heads of public school systems. Meyers says this data-driven approach to policy-making helped to safeguard the citycompared to the rest of Texas, the Austin area has suffered the lowest Covid mortality rates.

In the last year, we've probably advanced the art and science and applications of models as much as we did in probably the preceding decades, she says.

At the heart of Meyers groups models of Covid dynamics, which they run in collaboration with the Texas Advanced Computing Center, are differential equationsessentially, math that describes a system that is constantly changing. Each equation corresponds to a state that an individual could be in, such as an age group, risk level for severe disease, whether they are vaccinated or not and how those variables might change over time. The model then runs these equations as they relate to the likelihood of getting Covid in particular communities.

Differential equations have been around for centuries, and the approach of dividing a population into groups who are susceptible, infected, and recovered dates back to 1927. This is the basis for one popular kind of Covid model, which tries to simulate the spread of the disease based on assumptions about how many people an individual is likely to infect.

But Covid demanded that data scientists make their existing toolboxes a lot more complex. For example, Shaman and colleagues created a meta-population model that included 375 locations linked by travel patterns between them.

Using information from all of those cities, We were able to estimate accurately undocumented infection rates, the contagiousness of those undocumented infections, and the fact that pre-symptomatic shedding was taking place, all in one fell swoop, back in the end of January last year, he says.

The IHME modeling began originally to help University of Washington hospitals prepare for a surge in the state, and quickly expanded to model Covid cases and deaths around the world. In the spring of 2020, they launched an interactive website that included projections as well as a tool called hospital resource use, showing at the U.S. state level how many hospital beds, and separately ICU beds, would be needed to meet the projected demand. Mokdad says many countries have used the IHME data to inform their Covid-related restrictions, prepare for disease surges and expand their hospital beds.

As the accuracy and abundance of data improved over the course of the pandemic, models attempting to describe what was going on got better, too.

In April and May of 2020 IHME predicted that Covid case numbers and deaths would continue declining. In fact, the Trump White House Council of Economic Advisers referenced IHMEs projections of mortality in showcasing economic adviser Kevin Hassetts cubic fit curve, which predicted a much steeper drop-off in deaths than IHME did. Hassetts model, based on a mathematical function, was widely ridiculed at the time, as it had no basis in epidemiology.

But IHMEs projections of a summertime decline didnt hold up, either. Instead, the U.S. continued to see high rates of infections and deaths, with a spike in July and August.

Mokdad notes that at that time, IHME didnt have data about mask use and mobility; instead, they had information about state mandates. They also learned over time that state-based restrictions did not necessarily predict behavior; there was significant variation in terms of adhering to protocols like social-distancing across states. The IHME models have improved because data has improved.

Now we have mobility data from cell phones, we have surveys about mask-wearing, and all of this helps the model perform better, Mokdad says. It was more a function of data than the model itself.

Better data is having tangible impacts. At the Centers for Disease Control and Prevention, Michael Johansson, who is leading the Covid-19 modeling team, noted an advance in hospitalization forecasts after state-level hospitalization data became publicly available in late 2020. In mid-November, the CDC gave all potential modeling groups the goal of forecasting the number of Covid-positive hospital admissions, and the common dataset put them on equal footing. That allowed the CDC to develop ensemble forecastsmade through combining different modelstargeted at helping prepare for future demands in hospital services.

This has improved the actionability and evaluation of these forecasts, which are incredibly useful for understanding where healthcare resource needs may be increasing, Johansson writes in an e-mail.

Meyers initial Covid projections were based on simulations she and her team at the University of Texas, Austin, had been working on for more than a decade, since the 2009 H1N1 flu outbreak. They had created online tools and simulators to help the state of Texas plan for the next pandemic. When Covid-19 hit, Meyers team was ready to spring into action.

The moment we heard about this anomalous virus in Wuhan, we went to work, says Meyers, now the director of the UT Covid-19 Modeling Consortium. I mean, we were building models, literally, the next day.

Researchers can lead policy-makers to mathematical models of the spread of a disease, but that doesnt necessarily mean the information will result in policy changes. In the case of Austin, however, Meyers models helped convince the city of Austin and Travis County to issue a stay-at-home order in March of 2020, and then to extend it in May.

The Austin area task force came up with a color-coded system denoting five different stages of Covid-related restrictions and risks. Meyers team tracks Covid-related hospital admissions in the metro area on a daily basis, which forms the basis of that system. When admission rates are low enough, lower stage for the area is triggered. Most recently, Meyers worked with the city to revise those thresholds to take into account local vaccination rates.

But sometimes model-based recommendations were overruled by other governmental decisions.

In spring 2020, tension emerged between locals in Austin who wanted to keep strict restrictions on businesses and Texas policy makers who wanted to open the economy. This included construction work, which the state declared permissible.

Because of the nature of the job, construction workers are often in close contact, heightening the threat of viral exposure and severe disease. In April 2020, Meyers groups modeling results showed that the Austin areas 500,000 construction workers had a four-to-five times greater likelihood of being hospitalized with Covid than people of the same age in different occupational groups.

The actual numbers from March to August turned out strikingly similar to the projections, with construction workers five times more likely to be hospitalized, according to Meyers and colleagues analysis in JAMA Network Open.

Maybe it would have been even worse, had the city not been aware of it and tried to try to encourage precautionary behavior, Meyers says. But certainly it turned out that the risks were much higher, and probably did spill over into the communities where those workers lived.

Some researchers like Meyers had been preparing for their entire careers to test their disease models on an event like this. But one newcomer quickly became a minor celebrity.

Youyang Gu, a 27-year-old data scientist in New York, had never studied disease trends before Covid, but had experience in sports analytics and finance. In April of 2020, while visiting his parents in Santa Clara, California, Gu created a data-driven infectious disease model with a machine-learning component. He posted death forecasts for 50 states and 70 other countries at covid19-projections.com until October 2020; more recently he has looked at US vaccination trends and the path to normality.

While Meyers and Shaman say they didnt find any particular metric to be more reliable than any other, Gu initially focused only on the numbers of deaths because he thought deaths were rooted in better data than cases and hospitalizations. Gu says that may be a reason his models have sometimes better aligned with reality than those from established institutions, such as predicting the surge in in the summer of 2020. He isnt sure what direct effects his models have had on policies, but last year the CDC cited his results.

Today, some of the leading models have a major disagreement about the extent of underreported deaths. The IHME model made a revision in May of this year, estimating that more than 900,000 deaths have occurred from Covid in the U.S., compared with the CDC number of just under 600,000. IHME researchers came up with the higher estimate by comparing deaths per week to the corresponding week in the previous year, and then accounting for other causes that might explain excess deaths, such as opioid use and low healthcare utilization. IHME forecasts that by September 1, the U.S. will have experienced 950,000 deaths from Covid.

This new approach contradicts many other estimates, which do not assume that there is such a large undercount in deaths from Covid. This is another example of how models diverge in their projections because different assumed conditions are built into their machinery.

Covid models are now equipped to handle a lot of different factors and adapt in changing situations, but the disease has demonstrated the need to expect the unexpected, and be ready to innovate more as new challenges arise. Data scientists are thinking through how future Covid booster shots should be distributed, how to ensure the availability of face masks if they are needed urgently in the future, and other questions about this and other viruses.

We're already hard at work trying to, with hopefully a little bit more lead time, try to think through how we should be responding to and predicting what COVID is going to do in the future, Meyers says.

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What Data Scientists Learned by Modeling the Spread of Covid-19 - Smithsonian Magazine

When it comes to successful data science for SEO, nothing is more important than having the right team in place.

Challenges in obtaining and ensuring the consistency of the data, as well as in your choice of machine learning models and in the associated analyses, all benefit from having team members with different skill sets collaborating to solve them.

This article presents the three main types of teams, who is on them, and how they work.

Lets open the floor with that loneliest of data science SEO professionals the team of one.

The one-person team is often the reality in small and large structures alike. There are plenty of versatile people out there who can manage both the SEO and the data functions on their own.

The lone data science SEO professional can generally be described as an SEO expert who has decided to take advanced courses in computer science to focus on a more technical side of SEO.

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They have mastered at least one programming language (such as R or Python) and use machine learning algorithms.

They are closely following Google updates like Rankbrain, BERT, and MUM, as Google has been injecting increasingly more machine learning and AI into its algorithms.

These pros must be skilled in the automation of SEO processes to scale their efforts. This might include:

In my organization, we share these SEO use cases in the form of a Jupyter Notebook. However, it is easy to automate them using Papermill or DeepNote (which now offers an automatic mode to launch Jupyter Notebooks regularly) in order to run them daily.

If you want to mix it up and enhance your professional value, there are excellent training courses for SEO enthusiasts to learn data science and conversely, for data scientists to learn SEO, as well.

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The only limit is your motivation to learn new things.

Some prefer working alone; after all, it eliminates any of the bureaucracy or politics you might (but dont necessarily have to) find in larger teams.

But as the French proverb goes: Alone we go faster; together we go further.

Even if projects are completed quickly, they may end up as successful as they could have had there been a wider range of skills and experience at the table.

Now, lets leave the solitary SEO and move on to teams of two people.

You may already know MVP as a Minimum Viable Product. This format is widely used in agile methods where the project starts with a prototype that evolves in one- to three-week iterations.

The MVT is the equivalent for a team. This team structure can help minimize the risks and costs of the project even while bringing more diverse perspectives to the table.

It consists of creating a team with only two members with complementary skill sets often an SEO expert who also understands the mechanisms of machine learning, and a developer who tests ideas.

The team is formed for a limited period of time; typically about 6 weeks.

If we take content categorization for an ecommerce site, for example, often one person will test a method and implement the most efficient one.

However, an MVT could perform more complex tests with several models simultaneously keeping the categorization that comes up the most often and adding image categorization, for example.

This can be done automatically with all existing templates. The current technology makes it possible to reach 95% of correct results, beyond which point the granularity of the results comes into play.

PapersWithCode.com can help you stay up to date with the current state of technology in each field (such as text generation), and will most importantly provide the source code.

GPT-3 from OpenAI, for example, can be used for prescriptive SEO to recommend actions for text summarization, text generation, and image generation, all with impressive quality.

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Come back in time with me for a moment and lets take a look at one of the best collaborations of all time: The A-Team.

Everyone on this iconic team had a specific role and as a result, they succeeded brilliantly in each of their collective missions.

Unfortunately, there were no episodes on SEO. But what might your data science SEO task force look like?

You will surely need an SEO expert working closely with a data scientist and a developer. Together, this team will run the project, prepare the data, and use the machine learning algorithms.

The SEO expert is best positioned to double as a project manager and handle communication with management and external stakeholders. (In larger companies, there may be dedicated roles for the teams manager and project leader.)

Here are several examples of projects that this type of team might be responsible for:

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Of course, teams need tools to maximize their efforts. This brings us to the idea of data SEO-compliant software.

I believe there are three principles to adhere to carefully here in order to avoid using black-box tools that give you results without explaining their methodologies and algorithms.

1. Access to documentation that clearly explains the algorithms and parameters of the machine learning model.

2. The ability to reproduce the results yourself on a separate dataset to validate the methodology. This doesnt mean copying software: all the challenges are in the performance, security, reliability, and industrialization of machine learning models, not in the model or the methodology itself.

3. The tool must have followed a scientific approach by communicating the context, the objectives, the methods tested, and the final results.

Data SEO is a scientific approach to optimizing for search that relies on data analysis and the use of data science to make decisions.

Whatever your budget, it is possible to implement data science methods. The current trend is that concepts used by data scientists are becoming increasingly accessible to anyone interested in the field.

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It is now up to you to take ownership of your own data science projects with the right skills and the teams. To your data science SEO success!

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3 Types of Data Science SEO Teams and How They Work - Search Engine Journal

BOULDER, Colo., June 16, 2021 /PRNewswire/ --DrivenData, the host of data science competitions that advance solutions for social good, and HeroX, the social network for innovation and the world's leading platform for crowdsourced solutions, today announced the winners of the third and final sprint of the Algorithm Contest of the Differential Privacy Temporal Map Challenge, which was sponsored by the Public Safety Communications Research (PSCR) Division of the National Institute of Standards and Technology (NIST).

With a prize purse totaling $161,000 across the entire challenge, today's announcement of the third algorithm sprint offered $25,000 to the first place winner. The team, "N - CRiPT", a group of differential privacy researchers from the National University of Singapore, Alibaba Group, secured first place. Their goal was to bring differential privacy into a practical setting. The second place winner was the "Minutemen" team, a group of differential privacy graduate students from the University of Massachusetts Amherst.

The focus of this prize challenge was to create synthetic data that preserves the characteristics of a dataset containing time and geographic information. Synthetic data has the ability to offer greater privacy protections than traditional anonymization techniques. Differentially private synthetic data can be shared with researchers, policy makers, and even the public without the risk of exposing individuals in the original data. However, the synthetic records are only useful if they preserve the trends and relationships in the original data.

Contestants of this challenge were charged with developing algorithms that de-identify datasets while maintaining a high level of accuracy. This ensures the data is both private and useful. Top contestants of the final sprint demonstrated algorithms that produce records with both more privacy and greater accuracy than the typical subsampling techniques used by many government agencies to release records.

The first sprint featured data captured from 911 calls in Baltimore, MD made over the course of one year. Participants in this sprint were tasked with developing de-identification algorithms designed to generate privatized data sets using the monthly reported incident counts for each type of incident by neighborhood. Winners were announced here.

The second sprint used demographic data from the U.S. Census Bureau's American Community Survey which surveyed individuals in various U.S. states from 2012 to 2018.The data set included 35 different survey features (such as age, sex, income, education, work and health insurance data) for every individual surveyed.Simulated longitudinal data was created by linking different individual records across multiple years, which increased the difficulty of protecting each simulated person's privacy. To succeed in this sprint, participants needed to build de-identification algorithms by generating a set of synthetic, privatized survey records that most accurately preserved the patterns in the original data. Winners were announced here.

The third sprint centered around taxi rides taken in Chicago, Illinois.Because the sprint focused on protecting the taxi drivers rather than just their trips, competitors needed to provide privacy for up to 200 records per individual driver, a very challenging problem. They were evaluated over 77 Chicago community areas.The deidentified synthetic data needed to preserve the characteristics of taxi trips in each community area, the patterns of traffic between communities, as well as the population characteristics of taxi drivers themselves (typical working times and locations). The top two winning teams were each able to produce synthetic data that provided very strong privacy protection and was also more accurate for analysis than data protected by traditional privacy techniques such as subsampling.

Challenge participants are now eligible to earn up to $5000 for creating and executing a development plan that further improves the code quality of solutions and advances their usefulness to the public safety community. Participants can also earn the Open Source prize, an additional $4000, by releasing their solutions in an open source repository. Winning solutions will be those that meet differential privacy after being uploaded to an open source repository.

DrivenData is a social enterprise dedicated to bringing the data tools and methods that are transforming industry to the world's biggest challenges. As part of that work, DrivenData's competition platform channels the skills and passion of data scientists, researchers, and other quantitative experts to build solutions for social good. These online machine learning challenges are designed to engage a large expert community, connect them with real-world data problems, and highlight their best solutions.

HeroX is a social network for crowdsourcing innovation and human ingenuity, co-founded in 2013 by serial entrepreneur, Christian Cotichini and XPRIZE Founder and Futurist, Peter Diamandis. HeroX offers a turnkey, easy-to-use platform that supports anyone, anywhere, to solve everyday business and world challenges using the power of the crowd. Uniquely positioned as the Social Network for Innovation, HeroX is the only place you can build, grow and curate your very own crowd.

To learn about eligibility requirements, visit challenge.gov, and for additional information about the challenge, visit DrivenData.org.

NIST, a nonregulatory agency of the U.S. Department of Commerce, promotes U.S. innovation and industrial competitiveness by advancing measurement science, standards and technology in ways that enhance economic security and improve our quality of life. To learn more about NIST, visitNIST.gov.

To arrange an interview and/or any media inquiries with NIST, please contact Jennifer Huergo at (202) 309-1027 and jennifer.huergo@nist.gov.

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DrivenData and HeroX Announce Winners of NIST's Synthetic Data Challenge - The Grand Junction Daily Sentinel

SANTA CLARA, Calif.--(BUSINESS WIRE)--CLARA Analytics (CLARA), the leading provider of artificial intelligence (AI) technology in the commercial insurance industry, today announced that Heather H. Wilson has been named as Chief Executive Officer. The CLARA Board of Directors selected Wilson based on her long track record of outstanding leadership in insurance and various global industries, including more than a decade of executive experience in data, analytics and artificial intelligence specifically. Wilsons in-depth knowledge of CLARAs space in combination with her exceptional professional relationships and strong business acumen will be key to CLARAs ongoing success and future growth.

Underwriting profitability and claims excellence remain a focus for all carriers. CLARAs tools deliver insights and optimize performance through the companys unique AI/ML models, leading to improved claim outcomes and underwriting results for our clients. CLARAs product suite has already saved organizations millions of dollars as well as streamlined operations, said Wilson. When presented with this opportunity, it was clear CLARA is just getting started. I am excited to take the helm of CLARA at such a pivotal moment in the insurance industry with our differentiated products and continued investment in our platform.

Heathers insurance domain knowledge as well as deep data science and AI expertise make her the absolute ideal fit for CLARAs next stage of growth, said Andy Pinkes, Independent Board Member and Interim CEO at CLARA.

Wilson currently sits on Equifaxs Board of Directors, serving on the Audit Committee and Technology Committee. She is recognized as a world-class expert and pioneer in data, analytics and AI. Previously, Wilson was the Chief Data Officer of AIG, responsible for the firms enterprise data program and next-generation data infrastructure. While at AIG, she was named the Insurance Woman of the Year by the Insurance Technology Association for her data innovation work. Furthermore, she was appointed to the U.S. Treasury Financial Research Advisory Committee in Washington, D.C., in 2015 for her data program experience.

In addition, Wilson was Global Head of Innovation and Advanced Technology at Kaiser Permanente, responsible for overseeing the strategies and implementation of leading-edge, data-driven analytical programs. Outside of the insurance space, Wilson served as Chief Data Officer of Citigroup and Global Head of Decision Management, responsible for spearheading new analytical capabilities companywide. As Executive Vice President, Chief Data Scientist of L Brands, an American fashion retailer, Wilson led several transformational data-oriented initiatives.

Wilson has been a steady supporter of diversity. She launched the Kaiser Permanente Women in Technology group, focused on mentorship and retention for women in math, technology and science. She was an Executive Member of Citi4Women at Citigroup, leading predictive analytics around retention. At AIG, she launched Global Women in Technology and served as Executive Sponsor of Girls Who Code.

About CLARA Analytics

CLARA Analytics improves claims outcomes in commercial insurance with easy-to-use AI-based products. The companys product suite applies image recognition, natural language processing, and other AI-based techniques to unlock insights from medical notes, bills and other documents surrounding a claim. CLARAs predictive insight gives adjusters AI superpowers that help them reduce claim costs and optimize outcomes for the carrier, customer and claimant. CLARAs customers include companies from the top 25 global insurance carriers to large third-party administrators and self-insured organizations. Founded in 2016, CLARA Analytics is headquartered in Californias Silicon Valley. For more information, visit http://www.claraanalytics.com, and follow the company on LinkedIn and Twitter.

All brand names and solution names are trademarks or registered trademarks of their respective companies.

Tags: CLARA Analytics, Heather H. Wilson, insurance, commercial insurance, commercial auto insurance, artificial intelligence, AI, data science, predictive analytics, machine learning, insurtech, insuretech, data innovation, claims, workers comp, workers compensation, healthcare, Medicare, litigation, CEO, woman CEO, women in technology, girls who code, diversity, Equifax

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CLARA Analytics Names Heather H. Wilson as Chief Executive Officer - Business Wire