Senior Data Scientist

📌 Role OverviewWe are seeking an experienced and innovative Senior Data Scientist to lead the development of predictive, prescriptive, and generative AI models. In this role, you will act as a critical bridge between business strategy and advanced technical execution. You will extract actionable insights from large, complex datasets and scale machine learning models directly into production environments to solve real-world problems.🚀 Key ResponsibilitiesTechnical Execution & ModelingDesign and deploy advanced machine learning algorithms, statistical models, and time-series forecasts.Implement Generative AI strategies, including fine-tuning Large Language Models (LLMs) and building Agentic AI workflows.Write production-grade Python code utilizing distributed computing frameworks.Own the MLOps lifecycle, managing automated retraining pipelines, model registries, and inference strategies.Data Architecture & QualityExtract and clean massive datasets using structured SQL queries across cloud data warehouses.Establish validation frameworks to ensure high data quality, strict schema adherence, and accurate feature engineering.Standardise experimentation by building modular data models and managing the full lifecycle of A/B testing.Leadership & StrategyTranslate ambiguous business challenges into technical specifications and structured analytical frameworks.Mentor junior team members, providing code reviews, technical guidance, and career development support.Present complex findings and data stories to non-technical executive stakeholders and VP/C-level leaders.🛠️ Required Skills & QualificationsTechnical ProfileProgramming: Mastery of Python, standard ML libraries (Scikit-Learn, XGBoost), and deep learning frameworks (PyTorch, TensorFlow).Big Data: Proficiency with SQL and distributed frameworks like Apache Spark.GenAI Stack: Deep understanding of LLMs, RAG architectures, vector databases, and prompt orchestration tools.Cloud & DevOps: Experience with cloud ecosystems (AWS, GCP, or Azure) alongside Git and CI/CD pipelines.Mathematics: Solid foundation in predictive statistics, experimental design, calculus, and linear algebra.Professional ExperienceEducation: Bachelor’s, Master’s, or PhD in Computer Science, Data Science, Statistics, Mathematics, or a related quantitative discipline.Experience: Minimum 5 to 8+ years of professional experience in data science, analytics, or machine learning engineering.Track Record: Proven success in deploying multiple enterprise-grade ML models into live production systems.