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.
- Robust 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.

📌 Senior Data Scientist (India)
🏢 Good Co India
📍 India