About the job:

We are looking for a Senior Data Scientist to build predictive and diagnostic models for a large retail and direct-selling client. You will work with substantial structured sales, customer, marketing, and CRM datasets to explain business performance, predict customer behaviour, and help managers make better decisions.
You should enjoy investigating data as much as building models: questioning definitions, uncovering unexpected patterns, testing competing explanations and determining whether an apparent finding survives rigorous validation. This role requires experience with imperfect business data, statistical reasoning, and production delivery in Databricks.

Key responsibilities:

1. Lead data-quality assessments and exploratory data analysis across customer acquisition, orders, sales, and marketing datasets.
2. Investigate missing data, duplicate records, inconsistent identifiers, join inflation, incomplete customer histories, delayed data, and changing business definitions.
3. Quantify the impact of data-quality issues on model feasibility and accuracy.
4. Use statistical analysis to distinguish meaningful patterns from sampling noise, population changes, measurement artefacts, and confounding.
5. Build, benchmark, and validate models for purchase progression, churn, and customer value.

Note:

Advanced expertise with depth in at least two of the following areas is required:

1. Survival and repeat-event modelling: Kaplan-Meier estimates, Cox proportional hazards, and discrete-time hazard models; right-censoring, left-truncated histories, proportional-hazards diagnostics, competing events, and horizon-specific calibration.
2. Time series and forecasting: Seasonality, trend, autocorrelation, structural breaks, and changing exposure periods; experience with STL decomposition, forecasting models such as ETS/ARIMA or Prophet, state-space/Kalman methods, prediction intervals, and rolling-origin evaluation.
3. Hierarchical modelling: Multilevel models and partial pooling for customers nested within sponsors, teams, or markets; uncertainty-aware comparisons of small groups; and hierarchical forecast reconciliation across regional, market, and global totals.
4. Causal inference and experimentation: Randomised experiments, power and minimum detectable effect, confounding, propensity scores, overlap diagnostics, difference-in-differences, or doubly robust estimation; experience assessing incremental impact or uplift where treatment-assignment evidence supports it.
5. Customer value and recommendations: Horizon-specific revenue/value models, two-part models for zero-heavy outcomes, item-to-item or basket recommendations, chronological ranking evaluation, and cold-start handling.
6. Experience with clustering, PCA, robust anomaly detection, drift diagnostics such as PSI, and Monte Carlo simulation for decision uncertainty, when applied to a clearly defined business problem.

Who can apply:

Only those candidates can apply who:

1. have minimum 1 years of experience

Salary:

₹ 6,30,000 - 12,50,000 /year

Experience:

1 year(s)

Deadline:

2026-10-29 23:59:59

Other perks:

5 days a week

Skills required:

Python, SQL, Data Analytics, Machine Learning and Data Science

Other Requirements:

1. 1. Strong SQL and Python skills, with experience analysing large relational datasets, verifying join cardinality, and understanding data grain.
2. 2. Applied statistics and exploratory data analysis (EDA), including distributions, conditional relationships, confidence intervals, bootstrap methods, hypothesis testing, effect sizes, multiple comparisons, and selection bias.
3. 3. Understanding of why millions of rows may still provide limited independent evidence.
4. 4. Experience in customer and cohort analysis, including conversion and retention curves, recency/frequency/value features, repeat-purchase behaviour, outcome maturity, and changing customer populations.
5. 5. Experience with predictive modelling using logistic regression and gradient-boosted trees such as LightGBM or XGBoost.
6. 6. Ability to perform thoughtful feature engineering, regularisation, class weighting or sampling, and appropriate treatment of missing values.
7. 7. Strong understanding of rigorous model evaluation, including temporal validation, rolling backtests, customer/team grouping where appropriate, leakage audits, and preprocessing fitted only on training data.
8. 8. Ability to evaluate calibration, precision, or lift at operational capacity, and performance across markets and customer segments.
9. 9. Practical experience with model interpretation using SHAP and related methods, including understanding their limitations with correlated features.

About Company:

IT and Technology related products and services in the areas of Artificial Intelligence, Generative AI, Deep Learning, Data Science and Machine Learning, Cloud and Data Engineering.