Data Scientist 2

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Requirements:

• Masters’s degree, or foreign equivalent, in Mathematics, Statistics, Analytics, or closely related field plus two years experience in the job offered or a related occupation.
• Natural Language Processing and Sentiment Analysis, Tokenization
• Design of Experiments - Analysis of Variance, Analysis of Covariance
• Causal Inference for Quasi Experiments - Pre-Post Analysis, Difference in Difference (DiD), Propensity Score Matching (PSM), Synthetic Control (Coarsened Exact Matching)
• Machine Learning & Statistical Modeling – Linear Regression, Logistic Regression, Decision Trees, Random Forest, Clustering Algorithms - KNN, K-Means, Artificial Neural Network, SVM, Naive Bayes Classification technique, Association Rule Mining
• Product Analytics - Product Sense, A/B Testing and Experimentation, Data Analysis and Insights
• Statistics – Descriptive, Probability, Sampling Distributions (Chi Sq, T, F, Z), Hypothesis Testing, Statistical Inference (Non-Parametric Tests)
• Time Series Forecasting – Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing (ETS)
• Tools - Tableau, Looker SQL - Advanced
• SQL with Window functions (Rank determination, continuous distribution, population variance),
• Python (Numpy, Pandas, Scikit Learn, Matplotlib, Scipy, web crawling, Plotly), R (ggplot), PySpark

Responsibilities:

• Design and implement data and statistical models to gather, analyze and transform big data into consumer insights to impact product growth and strategy using Advanced Analytics and Data Science.
• Define and cultivate best practices in analytics instrumentation and experimentation.
• Synthesize large volumes of data with attention to granular details and present findings and recommendations to senior-level stakeholders.
• Set up A/B tests and perform statistical testing to assess the impact of new features before launch.
• Create and monitor KPI dashboards for user interaction with product features on app and web using BigQuery, R, Python and Tableau and Looker.
• Design and implement end-to-end data pipelines: work closely with stakeholders to build instrumentation and define dimensional models, tables or schemas that support business processes.
• Partner closely with product, engineering, and other business leaders to influence product and program decisions supported data analysis.