Data Scientist - Advanced Analytical Solutions

Description

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 : :

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