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EXL : Data Scientist
Brief Description of position:
Responsibilities
Be involved in the approach to the problem, structure and delivery of the solution.
Contribute to how analytical approach is structured for specification of analysis.
Solve business problems by applying advanced Machine Learning algorithms and complex statistical models on large volumes of data.
Contribute insights from conclusions of analysis that integrate with initial hypothesis and business objective. Independently address complex problems.
Extremely comfortable working with data, including managing large number of data sources, analyzing data quality, and pro-actively working with client’s data.
Become an expert in clients’ information assets.
Build, scale and deploy holistic data science products after successful prototyping.
Reformulate highly technical information into concise, understandable terms for presentations.
Doing research on advanced and better ways of solving the problems and inculcating new learnings to the team.
Qualifications
Master’s or Bachelor's degree in math, statistics, economics, computer engineering or related analytics field from tier 1 colleges with strong record of achievement.
2-10years’ experience with solid analytical skills and an entrepreneurial, hands-on approach.
Strong hands on programming skills in Python, SQL. Experience with large data sets and analytical tools.
Excellent understanding of traditional machine learning techniques and algorithms, such as K-means, KNN, SVM, Random Forests etc. Experience using ML, DL frameworks (such as Scikit-learn, XGBoost, TensorFlow, Keras etc.)
Strong experience in presenting analytical insights using powerpoint/excel. Case interview is expected.
Candidates who have published white papers, research papers in the field of computer science and data science are desirable.
Prior experience in management consulting and/or analytics based consulting is desirable.
Preferred domain expertise in Retail, Media or Sports.
Very strong analytical skills with the demonstrated ability to research and make decisions based on the day-to-day and complex customer problems required.
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