Rekruton Technologies

Job Role As a Data Scientist III, you will own the analytical strategy for a core product area and take it all the way to a governed, monitored&nbsp;production system at scale, not just a working notebook. This role owns model-serving workflows end-to-end: you will define and optimize&nbsp;the data pipelines and ETLs that feed your models, build models that hold up under real production load, and implement the monitoring&nbsp;and governance that keeps them trustworthy over time. You will also mentor other data scientists doing the same.
 Key Responsibilities ● Own the end-to-end analytical framework for a product area, i.e., from problem framing through production deployment and&nbsp;monitoring, with minimal oversight ● Apply LLMs, prompt engineering, and retrieval-augmented generation (RAG) to real product problems — e.g., translating complex&nbsp;targeting/attribution outputs into plain language insights, or building AI-assisted workflows for internal or client-facing tools. ● Evaluate when generative AI is the right tool versus when a simpler model or heuristic will outperform it. This role is expected to have&nbsp;informed judgment here, not default-to-LLM instincts ● Stay hands-on: personally build, code, and develop AI/ML solutions rather than only architecting and delegating ● Build models that scale to production data volumes and real-time serving loads, not just prototypes that work on a sample ● Define and optimize data pipelines, ETLs, and AI model-serving workflows for your domain; this is not handed off to a separate MLOps&nbsp;function ● Implement model monitoring and governance (drift detection, performance tracking, retraining triggers, audit trails) so that production&nbsp;models stay reliable and defensible under compliance scrutiny ● Design measurement approaches for problems where clean ground truth doesn't exist ● Mentor and provide technical guidance to Data Scientist I/II team members; review and approve their modeling and code before it ships. ● Design and defend experimentation approaches (A/B testing, causal inference, uplift modeling) that hold up under stakeholder and&nbsp;compliance scrutiny ● Drive adoption of better MLOps tooling, scalability practices, or modeling methods across the data science team ● Proactively identify whitespace opportunities in product roadmap and bring data-backed recommendations to stakeholders, not just answers to their questions.

Skills Required ● Education: B.Tech / MS in Computer Science, Data Science, or a related field. ● At least 5 years of hands-on experience in data science and machine learning (both) is a must have ● Hands-on MLOps experience is a must have. You should be comfortable owning a model from training through deployment,&nbsp;serving, and production monitoring, not handing it off once it works in a notebook. This includes experience with model-serving&nbsp;infrastructure, pipeline/ETL orchestration, and monitoring or governance tooling (drift detection, performance dashboards,&nbsp;retraining triggers) ● Proven experience building and scaling machine learning models to handle production data volumes, throughput, and latency&nbsp;requirements is a must have. This means designing for scalability up front, not retrofitting it after something breaks in&nbsp;production ● Hands-on experience with LLMs and RAG architectures is a must have. This includes practical experience with prompt&nbsp;engineering, embedding-based retrieval, and vector databases (e.g., Pinecone, FAISS, Weaviate), and frameworks like&nbsp;LangChain/LangGraph or AWS Bedrock ● Demonstrated ownership of at least one modeling project taken fully end-to-end into production, with measurable business&nbsp;impact ● Experience designing and running experiments (A/B testing, or causal inference methods such as DiD, IV, PSM, uplift modeling) is&nbsp;non-negotiable ● Track record of mentoring or reviewing the work of junior data scientists is a must have ● A strong knowledge of SQL, data structures, and query optimization at a level where you can review others' queries, not just&nbsp;write your own ● Experience fine-tuning generative AI models (not just calling APIs) for real-world applications is highly desirable ● Familiarity with AI agent orchestration and multi-step agentic workflows is a plus ● Prior experience in advertising technology, programmatic media buying, or another high-scrutiny/regulated data domain is highly&nbsp;advantageous and good to have. Personality &amp; Work Ethic ● A strategic thinker who sets technical direction for a problem area, not just aligns to one ● Comfortable operating in ambiguity where the "right" ground truth doesn't exist yet ● Excellent communication skills, with a track record of influencing product or technical decisions using data ● Genuine mentorship instinct; has grown at least one junior team member's skills, not just supervised their output ● Ownership mentality that extends past "the model works" to "the model is scalable, monitored, governed, and still trustworthy&nbsp;six months from now." ● Doesn't let a research rabbit hole delay a hard deadline