Data Scientist

We are seeking a hands-on and results-driven Data Science Specialist with 5-10 years of experience in designing and implementing advanced analytical solutions. The ideal candidate will have strong statistical foundations, demonstrated expertise in solving real-world business problems using Machine Learning, Data Science, and a track record of building and deploying data products. Exposure to NLP and Generative AI will be considered an advantage.

Key Responsibilities:

- Collaborate with cross-functional teams to translate business problems into data science use cases.

- Design, develop, and deploy data science models - including classification, regression, and ideally survival analysis techniques.

- Build and productionize data products that deliver measurable business impact.

- Perform exploratory data analysis, feature engineering, model validation, and performance tuning.

- Apply statistical methods to uncover trends, anomalies, and actionable insights.

- Implement scalable solutions using Python (or R/Scala), SQL, and modern data science libraries.

- Stay up to date with advancements in NLP and Generative AI and evaluate their applicability to internal use cases.

- Communicate findings and recommendations clearly to both technical and non-technical stakeholders.

Qualifications:

Education:

- Bachelor's degree in a quantitative field such as Statistics, Computer Science, Mathematics, Engineering, or a related discipline is required.

- Master's degree or certifications in Data Science, Machine Learning, or Applied Statistics is a strong plus.

Experience:

- 5+ years of hands-on experience in data science projects, preferably across different domains.

- Demonstrated experience in end-to-end ML model development, from problem framing to deployment.

- Prior experience working with cross-functional business teams is highly desirable.

Must-Have Skills:

- Statistical Expertise: Strong understanding of hypothesis testing, linear/non-linear regression, classification techniques, and distributions.

- Business Problem Solving: Experience translating ambiguous business challenges into data science use cases.

- Model Development: Hands-on experience in building and validating machine learning models (classification, regression, survival analysis).

- Programming Proficiency: Strong skills in Python (Pandas, NumPy, Scikit-learn, Matplotlib/Seaborn), and SQL.

- Data Manipulation: Experience handling structured/unstructured datasets, performing EDA, and data cleaning.

- Communication: Ability to articulate complex technical concepts to non-technical audiences.

- Version Control & Collaboration: Familiarity with Git/GitHub and collaborative development practices.

- Deployment Mindset: Understanding of how to build data products that are usable, scalable, and maintainable.

Nice-to-Have Skills:

- Experience with survival analysis or time-to-event modelling techniques.

- Exposure to Natural Language Processing (NLP) methods (e.g., tokenization, embeddings, sentiment analysis).

- Familiarity with Generative AI technologies (e.g., LLMs, transformers, prompt engineering).

- Experience with MLOps tools, pipeline orchestration (e.g., MLflow, Airflow), or cloud platforms (AWS, GCP, Azure).