Data Science Architect

Growexx is looking for smart and passionate Senior Data Scientist, who will empower Marketing, Product, Sales teams to make strategic, data-driven decisions. 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. Education and Experience B Tech or B. E. (Computer Science / Information Technology) 5 + years as a Data Scientist or similar roles. Analytical & Person Skills Must have good logical reasoning and analytical skills. Good Communication skills in English – both written and verbal. Demonstrate Ownership and Accountability of their work. Attention to details.