Senior Data Scientist (7+ Yr immediate only

Senior Data Scientist

Experience - 7+ years

Budget - 90k - 1L Lakh per month (in hand)

Location - Onsite-Gurgaon

Notice Period - Immediate (currently not working)

Must Have Skills:

•Degree in Statistics is Mandatory

•5 Years solid Experience in credit Risk Modelling and Financial Risk Modelling is Mandatory

NOT

Key Results Areas

•Data mining and identification of trends

•Communication of trends and insights to stakeholders

•Development of scorecards & predictive models

Responsibilities

•understand a business problem & translate into statistical one is a must.

• He/she should be able to decide on the best modeling/analysis techniques and provide actionable insights/ model algorithms to the business. 

Model development & Deep-dive Strategic Analysis:

•Data extraction: Extract data from appropriate sources. Require knowledge of databases.

•Data processing: Perform complex data processing (e.g. merging, sorting, data transformations).

•Profiling and analysis: Combine in-depth business knowledge, insights, and techniques such as data visualization, cross-tabs, statistical tests, and data mining techniques to identify risk segments, behavioral trends, product preferences, up-selling, cross-selling, and retention opportunities

•Model development: Apply standard and cutting-edge techniques (statistical modeling, data mining, predictive analytics, machine learning, optimization) to identify drivers of a business metric. Compare techniques using validation to arrive at the best model.

Model scoring and business applications of modeling/analytics:

•Insert developed models into the scoring process

•Improvise the models based on feedbacks from scoring process 

Communication, Coordination & Presentation:

•Work with a consultative mindset to gather requirements from other team professionals to identify the right requirements.

Skills:

•Extensive experience of statistical tools e.g. R, Python, VBA, Excel etc.

•Experience in statistical modelling using Univariate and Multivariate Regression, Generalized Regression Modelling, Time series forecasting, Non-Parametric methods, Logistic Regression, Survival Analysis, Factor Analysis, Clustering, CART, Decision Trees, CHAID, Linear Optimization, Hypothesis testing, Boosting Techniques, Factor Analysis, Scorecard Development etc

•Experience in risk scorecards, predictive modelling, machine learning etc.

•Ability to extract data from databases, clean and prepare for modelling.

Education:

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POINTS TO NOTE:

• All employments with Start and End dates
• All education with Start and End Dates
• All skillsets as asked for in the table below
• Project details with responsibilities