Data Scientist - Causal Inference Data Radian Technologies

7 - 12 years

25 - 40 Lacs

Hyderabad

Remote

Key Responsibilities Causal Inference & Experimentation Design and analyze experiments (A/B tests, observational studies) to identify causal relationships between variables. Apply advanced causal inference methods (e.g., propensity score matching, difference-in-differences, instrumental variables, DAGs) to estimate treatment effects. Use tools like DoWhy , CausalML , or EconML to model causal relationships. Forecasting & Multivariate Analysis Develop time-series forecasting models (e.g., ARIMA, Prophet, LSTM) to predict business metrics (sales, demand, churn). Perform multivariate regression analysis to identify key drivers of outcomes. Validate model accuracy and refine approaches based on feedback. Data Analysis & Visualization Clean, preprocess, and analyze large datasets to extract meaningful patterns. Build dashboards and reports to communicate findings to stakeholders using tools like Tableau, Power BI, or Python (Matplotlib/Seaborn). Collaboration Partner with product, marketing, and operations teams to define KPIs and design data-driven strategies. Translate complex technical results into actionable business recommendations. Technical Skills : Causal Inference : Proficiency in methods like propensity score matching, synthetic controls, and counterfactual analysis. Forecasting : Experience with time-series models (Prophet, ARIMA) and machine learning frameworks (Scikit-learn, TensorFlow). Programming : Strong Python/R skills (Pandas, NumPy, StatsModels, CausalInference). Data Wrangling : SQL proficiency for querying large datasets. Experience : 5+ years in data science, with a focus on causal inference and experimental design. Experience in industries like e-commerce, retail, or tech is a plus.

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