Senior Data Scientist

neoqubit

Bengaluru, India

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Posted 23 hours ago
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Job Description

We are seeking a PhD-trained statistician to develop, validate, and deploy demand forecasting and related predictive models for complex, large-scale systems. The role focuses on modeling demand under varying constraints, understanding substitution and recapture behavior, and supporting downstream planning and optimization decisions through robust statistical and probabilistic models. This position is ideal for a statistics PhD who enjoys applying rigorous statistical methods to real-world forecasting, decision support, and large-scale data problems.

The Core Responsibilities For The Job Include The Following

Demand Forecasting and Predictive Modeling:

Develop and maintain demand forecasting models at multiple levels of aggregation (e. g., temporal, product, market, or network level).
Model unconstrained vs. constrained demand, accounting for capacity limits, spillover effects, and substitution and recapture behavior.
Produce short- and medium-term forecasts over multiperiod horizons (e. g., daily to weekly forecasts).

Statistical Modeling And Inference

Apply a range of statistical techniques, including time-series models (e. g., ARIMA, state-space, and hierarchical time series); regression and generalized linear models; Bayesian models; and probabilistic forecasting.
Quantify forecast uncertainty and communicate confidence intervals and risk measures clearly to stakeholders.

Machine Learning For Forecasting

Develop and evaluate ML-based forecasting models, such as tree-based models, regularized regression, and neural networks (where appropriate).
Compare ML and classical statistical approaches, selecting models based on: Accuracy, stability, and interpretability.
Support hybrid approaches where ML outputs feed into downstream analytical or optimization models.

Model Validation And Performance Monitoring

Design rigorous model validation frameworks, including backtesting, cross-validation, and error decomposition.
Monitor model performance over time and diagnose drift or breakdowns in predictive accuracy.
Recommend model recalibration or redesign when underlying demand patterns change.

Collaboration With Optimization And Planning Teams

Work closely with optimization, planning, and analytics teams to: Use supply and demand forecasts as inputs to decision models.
Provide statistical insights on model assumptions and limitations.
Support integrated workflows where forecasting informs capacity allocation and scenario analysis.

Data Analysis And Insight Generation

Conduct exploratory data analysis on large datasets to identify demand patterns, structural changes, seasonality, and anomalies.
Translate statistical findings into actionable business insights.

Requirements

Strong statistical foundations with applied modeling experience.
Comfortable working with imperfect real-world data.
Thoughtful about uncertainty, assumptions, and model limitations.
Able to explain complex statistical ideas in simple, practical terms.
Interested in influencing decisions through high-quality forecasts.
Skills Required: Time Series Forecasting, Bayesian Statistics / Probabilistic Modeling, Python (statsmodels, scikit-learn, and PyMC), Demand Forecasting, Statistical Modeling, ARIMA / State Space Models, ML Model Validation, and Regression Modeling.

Education And Statistical Foundations

PhD in Statistics, Biostatistics, Applied Mathematics, Econometrics, or a closely related field.
Strong foundation in probability theory, statistical inference, regression modeling, and time-series analysis.

Forecasting And Modeling Experience

Hands-on experience building forecasting models for real-world datasets.
Familiarity with constrained vs. unconstrained demand modeling and substitution or recapture effects (broadly defined).
Ability to balance model accuracy, robustness, and interpretability.

Programming And Tools

Strong programming skills in Python.
Experience with relevant libraries (e. g., statsmodels, scikit-learn, PyMC, Stan, or equivalent).
Comfortable working with large, messy datasets.

Preferred / Nice To Have Qualifications

Experience working on forecasting or analytics problems in applied domains such as planning, resource allocation, and supply-demand systems.
Familiarity with optimization concepts and how forecasts are used in decision models.
Experience communicating statistical results to nontechnical audiences.

What Makes This Role a Good Fit for a Statistics PhD:

Strong emphasis on core statistical thinking and inference.
Opportunity to apply theory to real, impactful forecasting problems.
Balance between classical statistics and modern ML methods.
Clear pathway to influence downstream decision-making without owning optimization logic.
Structured, well-scoped problems with room for methodological depth.

This job was posted by Arulsakthi V from Neoqubit.

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About Company

neoqubitJob Source: www.linkedin.com

Job ID: 148888893

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Last Updated: 09-06-2026 08:30:40 PM

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