Senior Data Scientist

Role: Senior Data Scientist

Required Experience: 5 + years as a Data Scientist or similar roles

Key Responsibilities -

• Mine, process, and analyse hit/event level web, product, sales, and digital marketing data.
• Leverage LLMs (Large Language Models) and traditional machine learning to mine, process, and analyze web, product, sales, and digital marketing event-level data.
• Develop and fine-tune LLM-driven solutions for tasks such as text summarization, customer support automation, personalization, and user journey understanding.
• Build and deploy predictive models and ML algorithms across structured and unstructured customer profile, journey, and usage datasets.
• Deploy LLM and ML models into production environments for activation across websites, product applications, and sales/marketing channels.
• Design and implement model activation strategies, including A/B testing plans, benchmarking studies, and measurement of final business impact.
• Conduct comprehensive evaluation of LLMs, including performance benchmarking (accuracy, latency, token usage, cost), prompt effectiveness testing, fine-tuning impact analysis, and safety/bias assessments.
• Design, build, and deploy LLM-based agentic systems using frameworks such as LangChain, AutoGen, CrewAI, or custom orchestration for complex workflows (e.g., multi-agent collaboration, function-calling pipelines, dynamic task execution).
• Integrate LLM agents with APIs, internal knowledge bases, retrieval systems (RAG architectures), and external tools to enable autonomous or semi-autonomous decision-making.
• Partner with data engineering teams to enhance and maintain the Customer360 data model, including creating new feature engineering requirements, improving taxonomy, and identifying and resolving data quality issues.
• Collaborate with cross-functional teams (Enterprise Data Warehouse, Salesforce MOPS, IT, Product, Marketing) to continuously improve data integration and quality for advanced modeling use cases.
• Build a deep understanding of business models, objectives, challenges, and opportunities by working closely with leadership and key stakeholders.
• Document model methodologies, evaluation frameworks, agent workflows, deployment architectures, and post-activation performance results in a structured and reproducible format.
• Stay current with advancements in LLMs, agentic AI, retrieval-augmented generation (RAG), and ML technologies to recommend and implement innovative solutions.

Key Skills

• Experience using Python, SciKit, SQL, Snowflake, product usage data, Jupyter Notebooks, Amazon SageMaker, Airflow, Github.
• Proficient in data mining, advanced statistical analysis, feature engineering, and mathematical modeling.
• Deep experience with machine learning techniques including supervised, unsupervised, reinforcement learning, causal inference, and predictive modeling.
• Skilled across the full ML lifecycle: data preparation, feature creation and selection, model training, hyperparameter tuning, evaluation, and deployment for inference/prediction.
• Extensive hands-on experience with cookie-level advertising and digital marketing data (Google Ads, Bing, Epsilon, LinkedIn, Facebook) for demand generation KPIs such as ROAS, CTRs, impressions, multi-touch attribution (MTA).
• Proven experience designing, fine-tuning, evaluating, and deploying Large Language Models (LLMs) and generative AI applications.
• Experience designing and deploying agentic systems using frameworks such as LangChain, AutoGen, CrewAI, and custom function-calling pipelines.
• Expertise integrating LLM agents with APIs, knowledge bases, retrieval systems (RAG architecture), and orchestrating dynamic multi-agent workflows.
• Strong understanding of evaluation metrics for LLMs, including prompt testing, token optimization, bias/safety analysis, latency, and cost benchmarks.
• Deep familiarity with cookie-level web and product behavior data (usage metrics, conversion funnels, bounce rates, sessions, hits/events, journey optimization).
• Expertise in designing and executing A/B, multivariate, and lift tests to measure activated ML/LLM model performance across digital and offline channels.
• Skilled in gathering business requirements, translating them into ML use cases, and clearly communicating methodologies and results to both technical and non-technical stakeholders.
• Continuous learner, keeping up-to-date with the latest advances in transformers, generative AI models, retrieval-augmented generation (RAG), and agentic AI frameworks.
• Preferred: practical experience in an engineering capacity building, testing, deploying, and optimizing ensemble ML and LLM solutions in production environments.

Interested candidates are requested to share their updated resumes at - [email protected]