Impact Analytics - Senior Data Scientist - Statistical Modeling & Predictive Analytics

Description :

We are seeking a highly skilled and analytical Senior Data Scientist to design, develop, and deploy advanced data-driven solutions that support business decision-making.

The ideal candidate will have hands-on experience in statistical modeling, machine learning, and large-scale data analysis, along with the ability to translate complex data insights into actionable business outcomes.

Key Responsibilities :

- Design, develop, and deploy predictive and prescriptive analytics models using machine learning and statistical techniques.

- Analyze large, complex datasets to identify patterns, trends, and business insights.

- Build, validate, and optimize machine learning models including classification, regression, clustering, recommendation systems, and time-series forecasting.

- Collaborate with product managers, engineering teams, and business stakeholders to understand requirements and deliver data-driven solutions.

- Perform feature engineering, model evaluation, and hyperparameter tuning to improve model performance.

- Develop and maintain end-to-end data science pipelines from data ingestion to model deployment.

- Apply advanced statistical methods and experimentation techniques such as A/B testing and hypothesis testing.

- Present insights and model results to stakeholders using clear visualizations and storytelling.

- Mentor junior data scientists and provide technical guidance where required.

- Ensure best practices in data governance, model documentation, and reproducibility.

Required Skills & Technical Competencies :

Data Science & Machine Learning :

- Strong expertise in machine learning algorithms (linear/logistic regression, random forests, XGBoost, SVM, neural networks).

- Experience with deep learning frameworks such as TensorFlow, PyTorch, or Keras.

- Solid understanding of statistics, probability, and experimental design.

Programming & Tools :

- Proficiency in Python (NumPy, Pandas, Scikit-learn, Matplotlib/Seaborn).

- Experience with SQL for querying large datasets.

- Familiarity with big data tools such as Spark, Hive, or Databricks is a plus.

- Experience using Jupyter, Git, and ML lifecycle tools (MLflow, Airflow, etc.

Data Engineering & Deployment :

- Experience deploying models using APIs, cloud platforms, or containerization (Docker).

- Exposure to cloud environments such as AWS, Azure, or GCP.

- Understanding of MLOps practices and CI/CD for machine learning models is preferred.

Educational Qualifications :

- Bachelors or Masters degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field.

- Advanced certifications in Data Science or Machine Learning are an added advantage.

Experience :

- 3-6 years of hands-on experience in data science, machine learning, or advanced analytics roles.

- Proven experience delivering production-ready models with measurable business impact