Data Scientist

This role blends advanced Data Science, Machine Learning, and Generative AI with robust Data Engineering, particularly using Spark and AWS, to deliver scalable, production-grade analytical products. You will design end-to-end data solutions—from data ingestion to model deployment—while partnering with engineering and product teams to translate insights into actionable outcomes.

Roles & Responsibilities

• Lead the full ML development lifecycle: problem framing, hypothesis formulation, feature engineering, model development, validation, deployment, and monitoring.
• Develop, test, and optimize machine learning models including:
• Supervised & unsupervised learning
• Statistical modeling and forecasting
• Natural Language Processing (NLP)
• Generative AI techniques for automation and insight extraction
•  Graph/network analytics for analyzing network behaviors and relationships
• Build advanced anomaly detection, predictive maintenance, and risk scoring models for network security and operational efficiency.
• Conduct large-scale exploratory data analysis (EDA) to identify trends, data quality issues, and opportunities for automation.
• Define and implement model evaluation and A/B testing strategies.
• Collaborate with ML engineering teams to operationalize models using MLOps best practices.
• Communicate complex analytical findings through clear narratives, visualizations, and presentations tailored to technical and non-technical audiences.
  

Data Engineering & ETL

• Design, develop, and maintain scalable, fault-tolerant ETL pipelines using Spark to support analytics and machine learning workloads.
• Implement monitoring, alerting, and automated recovery mechanisms to ensure data pipeline reliability.
• Build robust feature pipelines that enable real-time and batch ML processing.
• Integrate data from a wide range of sources: APIs, Flat files , Relational databases and Distributed file systems (HDFS/S3)
• Support continuous integration and continuous delivery (CI/CD) workflows for data and ML components.

Required Qualifications

• Strong communication, presentation skills, and ability to translate analytics into business value.
• Expertise in programming languages commonly used in data science: Python (primary), Scala or Java (preferred for ETL/engineering).
• Proven experience with Spark and large-scale distributed data processing.
• Deep understanding of: Statistical modeling, Hypothesis testing, Experimental design and Causality and multicollinearity.
• Strong SQL skills and experience with relational and NoSQL databases.
• Expertise across a wide range of ML methodologies: Regression, classification, clustering, Time-series forecasting, Bayesian methods, NLP and text analytics and Graph analytics.
• Experience with data preprocessing, feature engineering, and EDA.
• Familiarity with data architectures such as data lakes, warehouses, and marts.
• Demonstrated ability to continuously learn, adapt, and share knowledge.

Preferred Qualifications

• Experience with AWS services (S3, EMR, Lambda, Glue, SageMaker).
• Prior exposure to Generative AI, LLMs, prompt engineering, or building AI-driven automation systems.
• Experience with Linux-based systems.
• Background in text mining, document classification, or large-scale unstructured data processing.
• Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, Physics, Engineering, Operations Research, or a related field.
• Master’s degree with 6+ years or Bachelor’s degree with 8+ years of relevant work experience.

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