Role Overview :

The role involves working with large and diverse datasets, developing robust analytical and machine learning models, generating actionable insights, and collaborating closely with product, engineering, and business teams.

Key Responsibilities :

Data Science &

- Machine Learning :
- Develop, train, validate, and deploy machine learning models to solve complex business and product problems.
- Apply supervised and unsupervised learning techniques to structured and unstructured datasets.
- Build predictive models for classification, regression, forecasting, recommendation, segmentation, and anomaly detection use cases.
- Select appropriate algorithms based on business objectives, data characteristics, and model performance requirements.
- Continuously improve model accuracy, scalability, robustness, and interpretability.

Statistical Analysis &

- Modelling :
- Apply statistical techniques to identify patterns, relationships, trends, and anomalies within large datasets.
- Perform hypothesis testing, statistical inference, correlation analysis, and experimentation.
- Design and analyse A/B tests and controlled experiments where required.
- Develop statistical and mathematical models to support business and product decisions.
- Evaluate model assumptions and ensure statistical validity of analytical outcomes.

Data Preparation &

- Feature Engineering :
- Work with data engineering teams to identify and prepare relevant data sources.
- Perform data cleaning, preprocessing, transformation, and exploratory data analysis.
- Develop meaningful features from raw data to improve model performance.
- Handle missing data, outliers, data inconsistencies, and other data-quality challenges.
- Build reusable data preparation and feature engineering pipelines.

Predictive Analytics :

- Develop predictive models to forecast business outcomes and identify future trends.
- Analyse customer, product, operational, and behavioural data to generate actionable insights.
- Identify opportunities for optimization through predictive and prescriptive analytics.




- Translate analytical findings into recommendations that can influence business and product strategy.

Model Evaluation &

- Optimization :
- Establish appropriate metrics to evaluate machine learning model performance.
- Perform model validation, cross-validation, error analysis, and performance benchmarking.
- Tune model hyperparameters and optimize algorithms for improved performance.
- Monitor model behaviour and identify model drift or degradation after deployment.
- Ensure models balance accuracy, interpretability, scalability, and business requirements.

Productionization &

- Deployment :
- Collaborate with data engineers and software engineers to productionize machine learning models.
- Develop production-ready data science solutions using Python and relevant ML frameworks.
- Create scalable model pipelines and APIs for integrating ML models into applications.
- Support model deployment, monitoring, testing, and lifecycle management.
- Follow engineering best practices around version control, testing, documentation, and reproducibility.

Business &

- Product Analytics :
- Partner with Product, Engineering, Business, and Operations teams to understand business problems and translate them into analytical solutions.
- Convert complex analytical findings into clear business recommendations.
- Present insights and model outcomes to technical and non-technical stakeholders.
- Identify new opportunities where data science and machine learning can create measurable business impact.

Research &

- Innovation :
- Stay current with developments in machine learning, statistical modelling, and data science.
- Evaluate new algorithms, frameworks,



and modelling techniques.
- Conduct proof-of-concepts and experiments to assess new approaches.
- Contribute to the development of reusable data science methodologies and best practices.

Required Skills :

- 5 - 10 years of skilled experience in Data Science, Machine Learning, Predictive Analytics, or a related field.
- Strong programming skills in Python.
- Strong understanding of Machine Learning algorithms and statistical modelling.
- Experience with supervised and unsupervised learning techniques.
- Strong knowledge of feature engineering, model evaluation, validation, and optimization.
- Hands-on experience with libraries/frameworks such as Scikit-learn, Pandas, NumPy, and relevant ML frameworks.
- Strong understanding of statistical concepts including hypothesis testing, probability, distributions, regression, and experimental design.
- Experience working with large datasets and complex data problems.
- Strong SQL skills for data extraction, transformation, and analysis.
- Experience taking machine learning models from development to production.
- Strong analytical and problem-solving capabilities.
- Excellent communication and stakeholder management skills.

Valuable to Have :

- Experience with Deep Learning and frameworks such as PyTorch or TensorFlow.
- Experience with cloud platforms such as AWS, GCP, or Azure.
- Exposure to distributed data processing technologies such as Spark.
- Experience with recommendation systems, forecasting, NLP, fraud detection, or anomaly detection.
- Knowledge of MLOps, model monitoring, CI/CD, and ML model lifecycle management.
- Experience working in a product-based technology environment.
- Experience solving large-scale business and customer analytics problems.

Qualifications :

- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative discipline.
- Strong hands-on experience applying data science to real-world business problems.

📌 Data Scientist - Machine Learning - Predictive Analytics (India)
🏢 Wenger u0026 Watson
📍 India