Data Scientist - Advanced Analytical Solutions

Description :

- 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 :

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

Experience :

- 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 :

- 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 :

- 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).