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Dreaming of working in a culture you love? We spoke with five people working in unique roles within Birmingham-based Protective and were surprised to see clear themes emerging from what they love about their jobs. Keep reading for all the details.

Blanton DePalma assists Protectives claims, asset protection division and mail center business areas in addressing their training needs.

The job requires intentional organization, advanced planning and a healthy mix of structure and flexibility to provide the level of support Protectives business colleagues require.

What does he wish hed known before joining Protective? How enthusiastic everyone on his team is. He finds their outlook contagious and energizing.

Looking for a new role? Protective is hiring.

Louise Ritter manages a team of Product Owners and Scrum Masters who work with Information Technology (IT) teams to deliver quality solutions as efficiently as possible.

As a people person her favorite aspect of the job is getting to meet and work with so many different personalities. She likes problem-solving and helping to enable others success.

I remember when I first joined six years ago, being really impressed by the amount of pride our employees associate with being part of Protective. We have a very strong culture, and our employees are at the center of that. Protective is invested in giving back in so many ways, which inspires a level of commitment you dont see everywhere.

Aquilla Jackson leads a solution team that supports those who sell Protectives products directly to the customer.

Case Managers are responsible for the initial review of life insurance applications, as well as establishing and maintaining internal and external distribution team relationships. She loves that each day is different and gives her the opportunity to learn something new. She enjoys leading people and building relationships.

I love, love, love that the culture of Protective is family. In New Business, we pride ourselves in having a sense of family. Aside from our real families, we spend the most time with our coworkers. It feels amazing to show up to a place every day knowing that you have the same level of support here as you do at home.

Raja Chakarvorty is the Chief Data Scientist at Protective, leading the Enterprise Data Science team and function. His team analyzes large amounts of data using advanced analytics techniques to create a best-in-class customer experience and a targeted sales process with superior risk selection.

His expertise is to build data science teams and capabilities from the ground up. Protective offered him that opportunity, which was very aligned with his passion.

Data is our lifeline, and it comes to us in broadly two formsstructured (rows and columns) and unstructured (pdfs, text, voice, images). Without data I cannot do any science! I have a motto No data left behind!

He finds that recruiting, training and growing the team has been unexpectedly rewarding.

Because theyre a fast-growing team, his role challenges him in many ways.

The innovation and business value creation invigorates me and drives me to get more business for my team. I love that!

Before applying and relocating from Hartford, Connecticut, he wishes hed known what incredible people he would get to work with each day and how cosmopolitan the city of Birmingham is.

Trenaye Baileys role enables a portion of the marketing team to achieve their goals by effectively utilizing print, tradeshows, multi-touch campaigns and digital media to increase sales productivity and generate leads for financial professionals.

She also specializes in ensuring they deliver tools, collateral and digital content that makes it easier for financial professionals to do business with and for Protective.

This role challenges her because no day is the same. This is mainly due to the growth of the company and the changing needs of the audiences.

I wish before applying, I would have known about the great culture and great people that work at Protective. I honestly wish I would have applied for Protective fresh from college. Protective cultivates their employees and treats them like family.

Protective is hiring. Apply today.

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Protective refers to Protective Life Corporation, and insurance company subsidiaries including Protective Life Insurance Company (Nashville, TN) and Protective Life and Annuity Insurance Company (Birmingham, AL). Protective is a registered trademark of Protective Life Insurance Company.

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5 unique jobs at Protective with openings now - Bham Now

We have further strengthened our ties to The Alan Turing Institute and our connections with other top-ranking universities by becoming a member of the Institutes newly launched Turing University Network.

The Alan Turing Institute is the national institute for data science and artificial intelligence and the Turing University Network forms part of its new strategy aimed at using data science and Artificial Intelligence (AI) for social good.

Our research in data science and AI is recognised as world leading and our researchers are tackling real-world challenges.

Last year, we were named among the first-ever successful applicants to The Alan Turing Institutes Network Development Awards, in recognition of our proven research excellence in data science and AI.

We have a significant research portfolio in data science and AI that supports pioneering multi-disciplinary data science research.

Notable research carried out at our globally outstanding University includes innovative sensing technology for self-driving cars, next generation airport scanners, automated visual surveillance and using the ExaHyPE simulation engine to power the oneAPI programming model.

Additionally, we are expanding the Turing portfolio in areas such as AI in education, digital theology and digital humanities.

Over the years, our Computer Science Department has expanded with more AI-facing research groups, such as the Artificial Intelligence and Humans Systems group (AIH); Scientific Computing (SciComp); Vision, Imaging and Visualisation (VIViD); beside the original Algorithms and Complexity group (ACiD).

Recently, our strength in the AI in Education area was recognised, with Durham being selected to host two major international conferences: EDM22 and the CORE-A AIED22; as well as co-hosting the Turing Artificial Intelligence in Education Event22.

As a research-intensive university, we are heavily involved in important regional initiatives such as the N8 Research Partnership, representing the eight most research-intensive universities in the North of England.

Within the N8 Centre of Excellence in Computationally Intensive Research (N8CIR), we visibly support the high-performance computing community, by hosting the BEDE supercomputer and key research areas of Digital Humanities, Digital Health, and recently Machine Learning.

Being a member of the Turning University Network will enable us to engage and collaborate with other UK universities with an interest in data science and AI both within The Alan Turing Institute and its broader networks.

Go here to read the rest:

We have joined Turning University Network - Mirage News

The field of artificial intelligence and machine learning is growing rapidly, and with it comes a high demand for skilled professionals. If you are interested in becoming an AI/ML engineer, this guide will provide you with the necessary steps to get started.

AI/ML engineers are responsible for developing and implementing machine learning models that can make predictions or decisions based on data. These models are used in a wide range of applications, from self-driving cars to medical diagnosis.

Becoming an AI/ML engineer requires a combination of technical skills, practical experience, and a deep understanding of the field. This guide will help you navigate the path to becoming an AI/ML engineer.

Before diving into AI/ML engineering, it is essential to understand the basics of data science. Data science is the foundation of machine learning, and it involves collecting, cleaning, analyzing, and visualizing data.

There are many resources available to learn data science, including online courses, books, and tutorials. Some popular tools for data science include Python, R, and SQL.

To get started with data science, it is essential to understand statistics, linear algebra, and calculus. These mathematical concepts are the building blocks of machine learning models.

Related: Artificial Intelligence (AI) Vs Artificial General Intelligence (AGI)

There are many programming languages used in AI/ML engineering, but some of the most popular ones include Python, R, Java, and C++. Python is the most commonly used language in the field of data science because it has a wide range of libraries and tools for machine learning.

Learning a programming language takes time and practice, but there are many online courses and tutorials available to help you get started. It is also essential to practice coding on your own and work on personal projects to build your skills.

Once you have learned a programming language, it is essential to become familiar with popular machine-learning libraries, such as TensorFlow, Keras, and Scikit-learn. These libraries provide pre-built models and tools for developing machine-learning applications.

Machine learning is a complex field, and it is essential to understand the underlying concepts before diving into AI/ML engineering. Some of the most important concepts to learn include supervised learning, unsupervised learning, and reinforcement learning.

Supervised learning involves training a model on labeled data to make predictions or decisions on new data. Unsupervised learning involves training a model on unlabeled data to identify patterns or clusters. Reinforcement learning involves training a model to make decisions based on rewards or punishments.

It is also essential to understand the different types of machine learning algorithms, such as decision trees, neural networks, and support vector machines.

Related: In the Age of AI: A Beginners Introduction to Artificial Intelligence

One of the best ways to develop your skills as an AI/ML engineer is to work on personal projects. These projects can be anything from predicting stock prices to developing a chatbot.

Data science competitions, such as Kaggle, provide an excellent opportunity to test your skills and learn from other professionals in the field. These competitions involve developing machine learning models to solve real-world problems.

Building your own projects allows you to apply the concepts you have learned and gain practical experience. It also provides you with a portfolio of work to showcase to potential employers.

When building your projects, it is essential to keep in mind the best practices for developing machine learning models, such as data preprocessing, model selection, and evaluation.

Participating in data science competitions allows you to work on challenging problems and gain exposure to the latest techniques and tools used in the field. It also provides you with an opportunity to network with other professionals in the field.

Winning a data science competition can also be a valuable addition to your portfolio and resume.

Attending industry conferences and meetups is an excellent way to stay up-to-date with the latest trends and techniques in AI/ML engineering. These events provide an opportunity to network with other professionals in the field and learn from experts.

Some popular conferences and meetups for AI/ML engineering include the annual Conference on Neural Information Processing Systems (NeurIPS), the International Conference on Machine Learning (ICML), and the TensorFlow Meetup.

Attending these events can also be a valuable addition to your resume and demonstrate your commitment to the field.

Related: AI in Law: Top Software for Legal Firms in 2023

Internships and entry-level positions are excellent ways to gain practical experience in AI/ML engineering. These positions provide an opportunity to work on real-world projects and learn from experienced professionals.

Some companies that offer internships and entry-level positions in AI/ML engineering include Google, Microsoft, and Amazon. It is also essential to check with local startups and companies in your area that may be hiring.

When applying for these positions, it is essential to showcase your skills and experience through a well-crafted resume and portfolio.

The field of AI/ML engineering is constantly evolving, and it is essential to keep learning and staying up-to-date with the latest trends and techniques.

Some ways to continue learning include reading research papers, taking online courses, and attending industry conferences and meetups. It is also essential to keep practicing your coding skills and working on personal projects.

Staying up-to-date with the latest tools and techniques in the field can also provide you with a competitive edge in the job market.

Related:

Becoming an AI/ML engineer requires a combination of technical skills, practical experience, and a deep understanding of the field. By following the steps outlined in this guide, you can get started on the path to becoming an AI/ML engineer.

Remember to continue learning and staying up-to-date with the latest trends and techniques in the field. With dedication and hard work, you can build a successful career in AI/ML engineering.

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Ultimate Guide to Kickstarting Your Career as an AI/ML Engineer - Dignited

Changing Commercial Models

The past 5 years have ushered in a new era for Life Science Commercial Models. The explosion of volume and variety of data sources available, coupled with advancements to Machine Learning and Artificial Intelligence based platforms, has unlocked unforeseen opportunities. However, post-pandemic economic instability has also left a mark, making it all the more important that companies choose the correct opportunities. Although their respective business strategies and tactics to address these opportunities may vary, one thing remains constant across the entire industry: the need for a strong data governance program to enable the maximization of Commercial ROI, while simultaneously protecting its most critical input - data.

A holistic Data Governance & Stewardship (DG&S) program is predicted on 5 core elements.

In the remainder of this blog, we will focus on how a data governance program directly supports maximizing the value of Life Sciences commercial operating model.

To support competitive differentiation, Commercial Life Science is increasingly focusing on deploying next-generation use cases such as AI&ML-based Next Best Action, Dynamic Segmentation, and Advanced Digital Channel Optimization to increase customer centricity and ultimately, revenue. These use cases require the integration of multiple data feeds from a plethora of internal and external sources. Robust data governance ensures the existence of data quality checks at both a source level and downstream, to ensure a steady ongoing stream of high-quality data is feeding these use cases. This in turn increases the reliability, validity and resulting financial value, generated by the insights.

Most organizations have suboptimal policies and processes in place for managing data. These often involve convoluted data quality maintenance processes to support data cleaning, where business teams interspersed throughout the organization work with stewards on an ad-hoc or per needed basis to respond reactively to quality concerns. This model can result in duplicate process and learning efforts when it comes to onboarding new and managing existing data. A data governance program ensures that these inefficiencies are addressed by creating a stewardship model with clearly defined and allocated roles and responsibilities. Such a model should be as streamlined as possible, freeing up resources to work on more meaningful work.

Data compliance is of particular importance for pharma, in large part due to the elevated level of regulatory scrutiny and the constant evolution of regulations across different markets. Data governance plays a critical role in ensuring that through applicable policies, data is classified in accordance with its sensitivity, and that it is managed and restricted accordingly. It ensures that data is stored, archived, retained, and disposed in a manner that complies with all relevant regulations. Failure to do so can result in crippling penalties, loss of reputation and disruption to business continuity.

Optimizing and operationalizing Data Governance programs can feel like a daunting endeavor, especially when it comes to identifying where to begin.

We recommend Life Science companies begin with assessing their current data governance maturity across the 5 core elements to better understand their challenges and more importantly, diagnose the root causes. The next step is to look at the activities leveraged by other Life Science peers to solve the same problems, and then contextualizing those activities to their specific situation. Lastly, we recommend defining an overall roadmap for data governance programs with a focus on launching quick strike pilots that can be scaled based on priorities.

IQVIA supports more than 40 global pharma, medical device and healthcare companies in their data governance and stewardship needs. If you are interested in learning firsthand about how Life Science companies have leveraged Data Governance to address their challenges, please connect with us here we would be happy to speak with you. You can learn more about IQVIAs Data Governance and Stewardship capabilities at our website.

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Is your Data Governance program unlocking the true potential of ... - IQVIA

With the rising interest in natural language processing, more and more practitioners are hitting the wall not because they cant build or fine-tune LLMs, but because their data is messy!

We will show simple, yet very effective coding procedures for fixing noisy labels in text data. We will deal with 2 common scenarios in real-world text data:

We will use ITSM (IT Service Management) dataset created for this tutorial (CCO license). Its available on Kaggle from the link below:

https://www.kaggle.com/datasets/nikolagreb/small-itsm-dataset

Its time to start with the import of all libraries needed and basic data examination. Brace yourself, code is coming!

Each row represents one entry in the ITSM database. We will try to predict the category of the ticket based on the text of the ticket written by a user. Lets examine deeper the most important fields for described business use cases.

If we take a look at the first two tickets, although one ticket is in German, we can see that described problems refer to the same software??Asana, but they carry different labels. This is starting distribution of our categories:

The help needed looks suspicious, like the category that can contain tickets from multiple other categories. Also, categories Outlook and Mail sound similar, maybe they should be merged into one category. Before diving deeper into mentioned categories, we will get rid of missing values in columns of our interest.

There isnt a valid substitute for the examination of data with the bare eye. The fancy function to do so in pandas is .sample(), so we will do exactly that once more, now for the suspicious category:

Bundled problems with Office since restart:

Messages not sent

Outlook does not connect, mails do not arrive

Error 0x8004deb0 appears when Connection attempt, see attachment

The company account is affected: AB123

Access via Office.com seems to be possible.

Obviously, we have tickets talking about Discord, Asana, and CRM. So the name of the category should be changed from Help Needed to existing, more specific categories. For the first step of the reassignment process, we will create the new column Keywords that gives the information if the ticket has the word from the list of categories in the Text column.

Also, note that using "if word in str(words_categories)" instead of "if word in words_categories" would catch words from categories with more than 1 word (Internet Browser in our case), but would also require more data preprocessing. To keep things simple and straight to the point, we will go with the code for categories made of just one word. This is how our dataset looks now:

output as image:

After extracting the keywords column, we will assume the quality of the tickets. Our hypothesis:

We made our new distribution and now is the time to examine tickets classified as a potential problem. In practice, the following step would require much more sampling and look at the larger chunks of data with the bare eye, but the rationale would be the same. You are supposed to find problematic tickets and decide if you can improve their quality or if you should drop them from the dataset. When you are facing a large dataset stay calm, and don't forget that data examination and data preparation usually take much more time than building ML algorithms!

outlook issue , I did an update Windows and I have no more outlook on my notebook ? Please help !

We understand that tickets from Outlook and Mail categories are related to the same problem, so we will merge these 2 categories and improve the results of our future ML classification algorithm.

Last, but not least, we want to relabel some tickets from the meta category Help Needed to the proper category.

We did our data relabeling and cleaning but we shouldn't call ourselves data scientists if we don't do at least one scientific experiment and test the impact of our work on the final classification. We will do so by implementing The Complement Naive Bayes classifier in sklearn. Feel free to try other, more complex algorithms. Also, be aware that further data cleaning could be done - for example, we could also drop all tickets left in the "Help Needed" category.

Pretty impressive, right? The dataset we used is small (on purpose, so you can easily see what happens in each step) so different random seeds might produce different results, but in the vast majority of cases, the model will perform significantly better on the dataset after cleaning compared to the original dataset. We did a good job!Nikola Greb been coding for more than four years, and for the past two years he specialized in NLP. Before turning to data science, he was successful in sales, HR, writing and chess.

More:

Dealing With Noisy Labels in Text Data - KDnuggets