ADCI

Applied Science, finance

SeniorAppliedScientist,FinAuto

₹25–45L ~AI est. Bengaluru, Karnataka, India FULL TIME
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Senior candidates.

The Brief

“Senior Applied Scientist, FinAuto at ADCI. Skills: Machine learning, Data science, Applied research, Model deployment. Use machine learning techniques. Use analytical techniques”

What You'll Achieve.

Provide better value; Optimize millions of transactions; Directly impact profitability; Build solutions to challenging problems; Prevent every TFAW transaction

Industry & Context.

Applied Science, finance
Problems you'll solve

Solve real world problems; Identify problems; Propose solutions

What They're Looking For.

Must Have

7+ years building ML models, Master's degree or PhD, 5+ years applied research, Experience programming Java, Experience programming C++, Experience programming Python, Experience with neural deep learning, Experience with machine learning

Nice to Have

Experience with R, Experience with scikit-learn, Experience with Spark MLLib, Experience with MxNet, Experience with Tensorflow, Experience with numpy, Experience with scipy, Experience with Hadoop, Experience with Spark

What You'll Do.

Use machine learning techniques

Use analytical techniques

Create scalable solutions

Analyze business data

Extract relevant information

Automate key processes

Optimize key processes

Implement novel approaches

Research novel approaches

Drive real-time model implementations

Optimize business operations

Establish automated processes

Develop models at scale

Validate models at scale

Implement models at scale

Maintain models at scale

Evangelize new algorithms

Drive implementation of ML models

Develop state-of-the-art solutions

Coordinate with Engineering team

Put models into production

Create operational efficiencies

How You'll Work.

Team & Collaboration

Software engineering teams; Business owners; Operations staff; Engineering teams; Product managers; Internal stakeholders; Cross-functional teams

Process & Methodology

Leading projects

Full Job Description

Do you want to join an innovative team of scientists who use machine learning and statistical techniques to create state-of-the-art solutions for providing better value to Amazon’s customers? Do you want to build and deploy advanced algorithmic systems that help optimize millions of transactions every day? Are you excited by the prospect of analyzing and modeling terabytes of data to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Machine Learning and Data Sciences team for FinAuto. If you have an entrepreneurial spirit, know how to deliver, love to work with data, are deeply technical, highly innovative and long for the opportunity to build solutions to challenging problems that directly impact the company's bottom-line, we want to talk to you. Major responsibilities - Use machine learning and analytical techniques to create scalable solutions for business problems - Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes - Design, development, evaluate and deploy innovative and highly scalable models for predictive learning - Research and implement novel machine learning and statistical approaches - Work closely with software engineering teams to drive real-time model implementations and new feature creations - Work closely with business owners and operations staff to optimize various business operations - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation - Mentor other scientists and engineers in the use of ML techniques Key job responsibilities Use machine learning and analytical techniques to create scalable solutions for business problems Analyze and extract relevant information from large amounts of Amazon’s hist

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