Multi-regression/Forecasting Data Scientist

Job Title: DS Multi-regression/forecasting

Location: Bangalore

Key Responsibilities

• Design and implement

  regression models

  and

  time series forecasting solutions

  to uncover key drivers of business performance.
• Design and implement

  regression, elasticity,

  and

  time series forecasting models

  to uncover demand drivers and inform

  pricing and revenue optimization decisions.

• Develop pricing and demand models (elasticity, response curves, uplift) to evaluate price changes and support dynamic pricing decisions across products, regions, or channels.
• Build interpretable models that balance

  statistical rigor with business clarity

  , focusing on

  coefficient interpretation and forecast accuracy

  .
• Incorporate

  external data sources

  (e.g., macroeconomic indicators, competitor spend, weather, vendor-sourced variables) into models — either as direct inputs or, where required, by building

  supporting forecasts

  for those variables.
• Conduct

  EDA

  to identify structural trends, seasonal patterns, anomalies, and inter-variable relationships.
• Translate technical outputs into

  concise Excel summaries

  and

  business-ready PowerPoint presentations

  for senior stakeholders.
• Collaborate with business teams to define hypotheses, validate drivers, and refine models based on feedback and changing context.
• Clearly articulate insights, assumptions, and limitations to ensure analytical solutions are

  adopted and trusted

  by business users.
• Stay abreast of industry practices in

  interpretable ML, regression diagnostics, and applied forecasting

  .

Required Qualifications

• 3–5 years of experience in

  data science, statistical modelling, or business analytics

  , with a proven focus on applied regression and forecasting.
• Experience working with pricing, demand modelling, or elasticity-based analyses, preferably in a commercial or revenue-focused context.
• Strong command over

  regression techniques

  (linear, logistic, regularized) and

  time series models

  (ARIMA, ETS, Prophet, LSTM etc.).
• Proficiency in

  Python

  (Pandas, Scikit-learn, Statsmodels, Seaborn/Matplotlib) for data manipulation, modelling, and visualization.
• Comfort with

  interpreting model coefficients

  , explaining trends, and quantifying business impact.
• High proficiency in

  Excel

  to create structured outputs and explore driver sensitivities.
• Experience building and delivering

  analytical presentations using PowerPoint

  .
• Strong communication and storytelling skills, with the ability to convey complex findings to non-technical stakeholders.

Preferred Qualifications

• Exposure to modelling with

  externally sourced variables

  and using

  proxy forecasting techniques

  where appropriate.
• Familiarity with

  forecast performance metrics

  and residual diagnostics analysis.
• Experience working in

  notebook-based environments

  (e.g., Jupyter, Databricks).
• Experience translating model outputs into pricing recommendations or decision frameworks for business users.
• Basic understanding of

  ETL/data engineering workflows

  or data joining across multiple sources.