Job Description:

We are looking for a highly motivated Specialist Data Scientist with 37 years of experience in Machine Learning, Commercial Analytics, and Sales Analytics.

The ideal candidate will leverage advanced statistical techniques, machine learning models, and optimization methods to drive business growth, improve commercial effectiveness, and support strategic decision-making.

The role requires expertise in predictive analytics, forecasting, experimentation, customer segmentation, and optimization, along with strong Python programming skills and the ability to translate business problems into scalable analytical solutions.

Key Responsibilities :

- Develop advanced analytics solutions to improve sales performance, pricing strategies, customer targeting, and revenue growth.

- Analyze commercial and sales data to identify opportunities, trends, risks, and growth drivers.

- Build data-driven frameworks for territory planning, sales force effectiveness, and market expansion strategies.

- Partner with business stakeholders to understand commercial challenges and recommend analytical solutions.

Design, develop, and deploy machine learning models for :

1. Customer propensity prediction

2. Churn prediction

3. Demand forecasting

4. Revenue prediction

5. Lead scoring

6. Product recommendation

- Implement classification, regression, and uplift models to support marketing and sales initiatives.

- Monitor model performance and continuously improve accuracy and business impact.

Forecasting & Time-Series Analytics:

- Build and maintain forecasting models for sales, demand, revenue, and business KPIs.

Apply statistical and machine learning techniques such as :

1. ARIMA/SARIMA

2. Prophet

3. XGBoost

4. Random Forest

5. LSTM (preferred)

- Improve forecast accuracy and automate forecasting processes.

- Develop customer and market segmentation frameworks using clustering techniques.

- Identify high-value customer segments and behavioral patterns.

- Create actionable insights for sales, marketing, and customer success teams.

- Develop optimization models for:

1. Trade spend optimization

2. Pricing optimization

3. Route optimization

4. Resource allocation

5. Promotion effectiveness

- Utilize linear programming and mathematical optimization techniques to improve business outcomes.

- Design and analyze A/B tests and controlled experiments.

- Apply causal inference methodologies to measure business interventions.

- Evaluate campaign effectiveness and business initiatives using statistical techniques.

- Build reusable analytics pipelines and model deployment workflows.

- Collaborate with data engineering teams to ensure data quality and availability.

- Automate reporting and analytical processes using Python.

- Present insights and recommendations to business leaders and stakeholders.

- Translate complex analytical findings into clear business recommendations.

- Work closely with Sales, Marketing, Finance, Product, and Commercial teams.

Required Technical Skills:

Programming:

- Strong hands-on experience in:

1. Python

2. SQL

Python Libraries:

1. Pandas

2. NumPy

3. Scikit-learn

4. Statsmodels

5. SciPy

6. XGBoost

7. LightGBM

8. Prophet

9. Matplotlib

10. Seaborn

Machine Learning:

1. Classification Models

2. Regression Models

3. Ensemble Methods

4. Feature Engineering

5. Model Evaluation & Validation

6. Hyperparameter Tuning

Statistical Techniques:

1. Hypothesis Testing

2. Experimental Design

3. Statistical Inference

4. Probability Theory

Time Series Forecasting:

1. ARIMA

- Data Science

- Statistics

- Mathematics

- Computer Science

- Economics

- Operations Research

- Engineering or related quantitative field

- Experience in Commercial Analytics, Sales Analytics, Revenue Growth Management, Pricing Analytics, or Marketing Analytics.

- Experience working with FMCG, Retail, Consumer Goods, Healthcare, Pharma, Telecom, or E-commerce domains.

- Exposure to production deployment of machine learning models.

- Experience handling large-scale structured and unstructured datasets.

- Improvement in sales forecasting accuracy.

- Revenue uplift through analytics-driven initiatives.

- Increased customer acquisition and retention.

- Improved campaign effectiveness through experimentation.

- Optimization-led cost savings and efficiency gains.

- Adoption of analytics solutions by business stakeholders