Senior Data Scientist

Role & responsibilities

• Develop, implement, and optimize machine learning models to solve complex business problems and improve decision-making.
• Perform exploratory data analysis (EDA), feature engineering, statistical analysis, and predictive modelling on large datasets.
• Build and evaluate supervised and unsupervised learning models, including classification, regression, clustering, and time-series forecasting.
• Apply deep learning, NLP, and Generative AI techniques to develop innovative data-driven solutions.
• Translate business requirements into analytical approaches, actionable insights, and measurable outcomes.
• Deploy, monitor, and maintain production-grade machine learning models in collaboration with data engineering and software engineering teams.
• Develop scalable data science workflows and pipelines using Python, SQL, and cloud-based technologies.
• Conduct hypothesis testing, A/B testing, experimentation, and statistical validation to measure model effectiveness.
• Communicate analytical findings, model performance, and recommendations to technical and non-technical stakeholders.
• Mentor junior data scientists, conduct code reviews, and promote best practices in model development, documentation, and reproducibility.
• Ensure data quality, model reliability, explainability, privacy, and compliance with relevant governance standards.
• Stay updated with emerging machine learning techniques, AI technologies, and industry best practices.

Preferred candidate profile

• 5 to 10 years of relevant experience in Data Science, Machine Learning, Advanced Analytics, or a related field.
• Bachelor's or master's degree in Computer Science, Data Science, Artificial Intelligence, Statistics, Mathematics, Engineering, or a related quantitative discipline.
• Strong proficiency in Python, SQL, statistics, probability, data manipulation, and exploratory data analysis.
• Hands-on experience developing, validating, and deploying machine learning models using libraries such as Scikit-learn, TensorFlow, or PyTorch.
• Strong understanding of feature engineering, model selection, hyperparameter tuning, cross-validation, and model evaluation metrics.
• Experience with NLP, deep learning, Generative AI, LLMs, and RAG architectures is an advantage.
• Working knowledge of cloud platforms such as AWS, Azure, or GCP, along with MLOps, Docker, CI/CD, and model monitoring.
• Experience with big data technologies, data pipelines, and distributed processing frameworks such as Apache Spark is preferred.
• Proven ability to independently manage end-to-end data science projects and deliver measurable business impact.
• Strong analytical thinking, problem-solving, communication, and stakeholder management skills.
• Ability to collaborate with cross-functional teams and mentor junior team members.
• Experience in production model deployment, experimentation, and translating complex analytical results into business recommendations is highly desirable.