Despite the pandemic, organisations and industries are looking to hire professionals for data science and analytics who are familiar with advanced statistical techniques, machine learning tools, among others. With the evolving Data Science and Analytics market, data scientists and AI practitioners should keep themselves abreast of the latest tools and trends in the field.

In this article, we list down 50 latest job openings in data science and analytics that opened just last week.

(The jobs are sorted according to the years of experience required).

Location: Bangalore

Skills Required: Machine learning, Python, data analysis, SQL, analytics, predictive modelling, natural language processing, deep learning, data science, etc.

Apply here.

Location: Bangalore

Skills Required: Deep learning frameworks such as TensorFlow for Keras, Python and basic libraries for machine learning such as sci kit-learn and pandas, visualising and manipulating big data sets, Open CV, Linux, etc.

Apply here.

Location: Bangalore

Skills Required: Business Intelligence and analysis, designing, testing, migration, production support and implement automation, visualisation tools like Power BI/Tableau, statistical packages for analysing datasets, Big data handling tools such as Python/R programming knowledge, etc.

Apply here.

Location: Telangana

Skills Required: Machine learning, text analytics, and statistical analysis methods, data mining, data wrangling, and data munging, R, Python, SAS, SPSS, Weka, etc.

Apply here.

Location: Mumbai

Skills Required: Customer segmentation, user profiling, churn analysis, Tableau, Looker, Qlik, SQL and MS Excel, R or Python, etc.

Apply here.

Location: Bangalore

Skills Required: Analytical skills, problem-solving skills, statistical computer languages (R, Python, SAS), statistical concepts, advanced machine learning techniques, optimisation techniques, distributed data/computing tools, etc.

Apply here.

Location: Hyderabad

Skills Required: Building and implementing predictive models using machine learning algorithms, Python, R, Hadoop and related Big Data technologies, etc.

Apply here.

Location: Remote

Skills Required: Python, R, AI/ML, data science techniques, programming, statistics, large datasets, etc.

Apply here.

Location: Hyderabad

Skills Required: Strong analytical background, instance writing scripts, web scraping, calling APIs, write SQL queries, Python scripting, business stakeholders data requirements, design and implement automation policies, etc.

Apply here.

Location: Bangalore

Skills Required: SQL and relational databases, workflows using Alteryx, Python, Hadoop and cloud computing like AWS, data acquisition and manipulation, modelling, and analysing data from core platforms (Eloqua, Salesforce, Enterprise Data Warehouse), etc.

Apply here.

Location: Bangalore

Skills Required: Statistics, Machine Learning, programming skills in various languages (Python, Scala, R), machine learning frameworks such as Scikit-Learn, H2O, Keras, TensorFlow, and Spark, Linux/OS X command line, version control software (git), and general software development, database, programming or scripting to enable ETL development, etc.

Apply here.

Location: Bangalore

Skills Required: C++, Object orientated programming and File-based Design, video surveillance domain and algorithms evaluation, Linux platform-based development, Python/Go programming, Kaffe, TensorFlow, JS Scripting, etc.

Apply here.

Location: Bangalore

Skills Required: Python, data manipulation, wrangling, cleansing, and analysis, large-scale datasets, Anaconda, Rstudio, Apache HTTP server, ETL pipelines, MS SQL Server 2008 and above, SQL queries, etc.

Apply here.

Location: Mumbai

Skills Required: HDFS, file, database, JSON, HTML, data warehousing & databases, Hadoop, Hive, Spark, SQL, Oracle, RDBMS, R, Python, etc.

Apply here.

Location: Mumbai

Skills Required: Data mining and machine learning techniques, complex statistical models, SQL, Linux, Python, and R, Big Data technologies like Hadoop, Hive, and/or MapReduce, Spark, Amazon Web Services, Google Cloud Platform, etc.

Apply here.

Location: Bangalore

Skills Required: Qiskit stack, full-stack quantum development, quantum computing, Python, delivery and test-driven development environments, etc.

Apply here.

Location: Bangalore

Skills Required: ML models, ETL process, data structure/algorithm, design patterns, and testing principles, fraud detection & credit risk assessment, etc.

Apply here.

Location: Kolkata

Skills Required: Advanced knowledge of applications programming, system flow and develop standards for coding, testing, debugging, and implementation, micro/macro designing and familiar with Unix Commands and basic work experience in Unix Shell Scripting, etc.

Apply here.

Location: Bangalore

Skills Required: Design, develop and enhance the Marcus Data Platform, data warehousing concepts, especially in the ETL space, advanced SQL knowledge and experience working with relational databases, query authoring (SQL), etc.

Apply here.

Location: Bangalore

Skills Required: Deep learning (e.g., CNN, RNN, LSTM), SQL, R/Python knowledge such as Python, data science libraries, version control, Tensorflow, Keras, Caffe, PyTorch, etc.

Apply here.

Location: Gurgaon

Skills Required: Python/R scripting, common ML/DL algorithms, AWS implementations, Spark, BI Tool, preferably DSS and Tableau, etc.

Apply here.

Location: Hyderabad

Skills Required: Researching, gathering and analysing data, statistical methods and procedures used to obtain data to ensure validity, applicability, efficiency, and accuracy, etc.

Apply here.

Location: Mumbai

Skills Required: Extract and visualise data using SQL queries, building predictive models on Python, etc.

Apply here.

Location: Mumbai

Skills Required: Satellite analytics, document digitisation, image-based recommender system, frameworks like Pytorch, Scikit-learn, Python programming, distributed computing, Azure / AWS / GCP, deep learning solutions using FastAPI, Docker, etc.

Apply here.

Location: Pune

Skills Required: ML libraries and applications, mining and analysing data, Deep Learning, Pyspark, implementing the A/B tests, etc.

Apply here.

Location: Bangalore

Skills Required: Python, SQL, cloud computing, ETL tools, Spark, Airflow, Kafka, Big query, etc.

Apply here.

Location: Bangalore

Skills Required: Data analysis, data mining, modelling, statistical analysis, etc.

Apply here.

Location: Delhi

Skills Required: SQL, Advance Excel, VBA, Python, R, SSRS, Tableau, Domo, Power BI, etc.

Apply here.

Location: Anywhere in India

Skills Required: Data analytics, SQL, analytical Skills & problem-solving skills, stakeholder management skills, etc.

Apply here.

Location: Bangalore

Skills Required: Analytical Skills and Process Orientation, R/Python, SQL, Excel, visualisation tools like R-Shiny, Tableau, etc.

Apply here.

Location: Bangalore

Skills Required: Databases, analytical reasoning, statistical techniques, MySQL, Postgres, Oracle databases and fluent in SQL scripts, XML, Javascript, or ETL frameworks, Excel, SPSS, SAS, etc.

Apply here.

Location: Hyderabad

Skills Required: Machine learning / statistical analytics,A/B testing, experiment design, causal inference, or quasi-experimental methods, etc.

Apply here.

Location: Bangalore

See the original post here:
50 Data Science and Analysts Jobs That Opened Just Last Week - Analytics India Magazine

You recently made a big discovery that an academic library containing millions of images used to train artificial intelligence systems had privacy and ethics issues, and that it included racist, misogynistic and other offensive content.

Yes, I worked on this with Vinay Prabhu a chief scientist at UnifyID, a privacy start-up in Silicon Valley on the 80-million images dataset curated by Massachusetts Institute of Technology. We spent about months looking through this dataset, and we found thousands of images labelled with insults and derogatory terms.

Using this kind of content to build and train artificial intelligence systems, including face recognition systems, would embed harmful stereotypes and prejudices and could have grave consequences for individuals in the real world.

What happened when you published the findings?

The media picked up on it, so it got a lot of publicity. MIT withdrew the database and urged people to delete their copies of the data. That was humbling and a nice result.

How does this finding fit in to your PhD research?

I study embodied cognitive science, which is at the heart of how people interact and go about their daily lives and what it means to be a person. The background assumption is that people are ambiguous, they come to be who they are through interactions with other people.

It is a different perspective to traditional cognitive science, which is all about the brain and rationality. My research looks at how artificial intelligence and machine learning has limits in how it can understand and predict the complex messiness of human behaviour and social outcomes.

Can you give me an example?

If you take the Shazam app, it works very well to recognise a piece of music that you play to it. It searches for the pattern of the music in a database, and this narrow search suits the machine approach. But predicting a social outcome from human characteristics is very different.

As humans we have infinite potentials, we can react to situations in different ways, and a machine that uses numerable parameters cannot predict whether someone is a good hire or at risk of committing a crime in the future. Humans and our interactions represent more than just a few parameters. My research looks at existing machine learning systems and the ethics of this dilemma.

How did you get into this work?

I started in physics back home in Ethiopia, but when I came to Ireland there was so much paperwork and so many exams to translate my Ethiopian qualification that I decided to start from scratch.

So I studied psychology and philosophy and I did a masters [masters course had lots of elements neuroscience, philosophy, anthropology, and computer science, where we built computational models of various cognitive faculties and it is where I really found my place.

How has Covid-19 affected your research?

At the start of the pandemic, I thought this might be a chance to write up a lot of my project, but I found it hard to work at home and to unhook my mind from what was going on around the world.

I also missed the social side, going for coffee and talking with my colleagues about work and everything else. So I am glad to be back in the lab now and seeing my lab mates even at a distance.

See the rest here:
Getting to the heart of machine learning and complex humans - The Irish Times

The decentralized finance (DeFi) space is growing rapidly. Oracle protocols like Chainlink, BAND and Gravity have experienced a significant increase in adoption in a cryptocurrency market thats still highly speculative and plagued by market manipulative and wash trading.

Fetch.ai, an open-access machine learning network established by former DeepMind investors and software engineers, has teamed up with Waves, an established, open-source blockchain protocol that provides developer tools for Web 3.0 applications.

As mentioned in an update shared with Crowdfund Insider:

[Fetch.ai and Waves will] conduct joint R&D for the purpose of bringing increased multi-chain capabilities to Fetch.ais system of autonomous economic agents (AEA). [They will also] push further into bringing DeFi cross-chain by connecting with Waves blockchain agnostic and interoperable decentralized cross-chain and oracle network, Gravity.

As explained in the announcement, the integration with Gravity will enable Fetch.ais Autonomous Economic Agents to gain access to data sources or feeds for several different market pairs, commodities, indices, and futures.

Fetch.ai and Waves aim to achieve closer integration with Gravity in order to provide seamless interoperability to Fetch.ai, making its blockchain-based AI and machine learning (ML) solutions accessible across various distributed ledger technology (DLT) networks.

As stated in the update, the integration will help with opening up new ways for all Gravity-connected communities to use Fetch.ais ML functionality within the comfort of their respective ecosystems.

As noted in another update shared with CI, a PwC report predicts that AI and related ML technologies may contribute more than $15 trillion to the world economy from 2017 through 2030. Gartner reveals that during 2019, 37% of organizations had adopted some type of AI into their business operations.

In other DeFi news, Chainlink competitor Band Protocol is securing oracle integration with Nervos, which is a leading Chinese blockchain project.

As confirmed in a release:

Nervos is a Chinese public blockchain thats tooling up for a big DeFi push. The project is building DeFi platforms with China Merchants Bank International and Huobi, and also became one of the first public blockchains to integrate with Chinas BSN. Amid the DeFi surge, Nervos is integrating Bands oracles to give developers access to real-world data like crypto price feeds.

Link:
AI and Machine Learning Network Fetch.ai Partners Open-Source Blockchain Protocol Waves to Conduct R&D on DLT - Crowdfund Insider

More promising is the idea of participation as justice. Here, all members of the design process work together in tightly coupled relationships with frequent communication. Participation as justice is a long-term commitment that focuses on designing products guided by people from diverse backgrounds and communities, including the disability community, which has long played a leading role here. This concept has social and political importance, but capitalist market structures make it almost impossible to implement well.

Machine learning extends the tech industrys broader priorities, which center on scale and extraction. That means participatory machine learning is, for now, an oxymoron. By default, most machine-learning systems have the ability to surveil, oppress, and coerce (including in the workplace). These systems also have ways to manufacture consentfor example, by requiring users to opt in to surveillance systems in order to use certain technologies, or by implementing default settings that discourage them from exercising their right to privacy.

Given that, its no surprise that machine learning fails to account for existing power dynamics and takes an extractive approach to collaboration. If were not careful, participatory machine learning could follow the path of AI ethics and become just another fad thats used to legitimize injustice.

How can we avoid these dangers? There is no simple answer. But here are four suggestions:

Recognize participation as work. Many people already use machine-learning systems as they go about their day. Much of this labor maintains and improves these systems and is therefore valuable to the systems owners. To acknowledge that, all users should be asked for consent and provided with ways to opt out of any system. If they chose to participate, they should be offered compensation. Doing this could mean clarifying when and how data generated by a users behavior will be used for training purposes (for example, via a banner in Google Maps or an opt-in notification). It would also mean providing appropriate support for content moderators, fairly compensating ghost workers, and developing monetary or nonmonetary reward systems to compensate users for their data and labor.

Make participation context specific. Rather than trying to use a one-size-fits-all approach, technologists must be aware of the specific contexts in which they operate. For example, when designing a system to predict youth and gang violence, technologists should continuously reevaluate the ways in which they build on lived experience and domain expertise, and collaborate with the people they design for. This is particularly important as the context of a project changes over time. Documenting even small shifts in process and context can form a knowledge base for long-term, effective participation. For example, should only doctors be consulted in the design of a machine-learning system for clinical care, or should nurses and patients be included too? Making it clear why and how certain communities were involved makes such decisions and relationships transparent, accountable, and actionable.

Plan for long-term participation from the start. People are more likely to stay engaged in processes over time if theyre able to share and gain knowledge, as opposed to having it extracted from them. This can be difficult to achieve in machine learning, particularly for proprietary design cases. Here, its worth acknowledging the tensions that complicate long-term participation in machine learning, and recognizing that cooperation and justice do not scale in frictionless ways. These values require constant maintenance and must be articulated over and over again in new contexts.

Learn from past mistakes. More harm can be done by replicating the ways of thinking that originally produced harmful technology. We as researchers need to enhance our capacity for lateral thinking across applications and professions. To facilitate that, the machine-learning and design community could develop a searchable database to highlight failures of design participation (such as Sidewalk Labs waterfront project in Toronto). These failures could be cross-referenced with socio-structural concepts (such as issues pertaining to racial inequality). This database should cover design projects in all sectors and domains, not just those in machine learning, and explicitly acknowledge absences and outliers. These edge cases are often the ones we can learn the most from.

Its exciting to see the machine-learning community embrace questions of justice and equity. But the answers shouldnt bank on participation alone. The desire for a silver bullet has plagued the tech community for too long. Its time to embrace the complexity that comes with challenging the extractive capitalist logic of machine learning.

Mona Sloane is a sociologist based at New York University. She works on design inequality in the context of AI design and policy.

Excerpt from:
Participation-washing could be the next dangerous fad in machine learning - MIT Technology Review

Machine learning is transforming the way an Air Force office analyzes and certifies new flight configurations.

The Air Force SEEK EAGLE Office sets standards for safe flight configurations by testing and looking at historical data to see how different storeslike a weapon system attached to an F-16affect flight. A project AFSEO developed along with industry partners can now automate up to 80% of requests for analysis, according to the offices Chief Data Officer Donna Cotton.

The application is kind of like an eager junior engineer consulting a senior engineer, Cotton said. It makes the straightforward calls without any input, but in the hard cases it walks into the senior engineers office and says: Hey, I did a bunch of research and this is what I found out. Can you give me your opinion?

Cotton spoke at a Tuesday webinar hosted by Tamr, one of the industry partners involved in the project. Tamr announced July 30 AFSEO awarded the company a $60 million contract for its machine learning application. Two other companies, Dell and Cloudera, helped AFSEO take decades of historical data from simulations, performance studies and the like that were siloed across various specialities and organize them into a searchable data lake.

On top of this new data architecture, the machine learning application provided by Tamr searches through all the historical data to find past records that can help answer new safety recommendation requests automatically.

This tool is critical because the vast majority of AFSEOs flight certification recommendations are made by analogy, meaning using previous data rather than new flight tests. But in the past, data was disorganized and lacked unification. This made tracking down these helpful records a challenge for engineers.

Now, a cleaner AFSEO data lake cuts the amount of time engineers waste on looking for the information they need. Machine learning further speeds up the process by generating safety reports automatically while still keeping the professional engineers in the loop. Even when engineers need to produce original research, the machine learning application can smooth the process by collecting related records to serve as a jumping off point.

The new process helps AFSEO avoid doing costly flight tests while also increasing confidence that the team is making the safety certification correctly with all the information available to them, Cotton said.

We are able to be more productive, Cotton said. It's saving us a lot of money because for us, it's not about profit, but it's about hours. It's about how much effort are we going to have to use to solve or to answer a new request.

See original here:
Air Force Taps Machine Learning to Speed Up Flight Certifications - Nextgov