Hi,
Greetings of the day!!
Senior Data Scientist – GenAI / RAG
Location - Houston TX / Santa Clara CA
3 rounds of internal technical interviews
Willing to attend an in-person interview at Santa Clara, CA / Houston TX office
Must Have -
- 5 to 7 years of Relevant experience is needed
- Hands-on Agentic AI production experience — LangGraph, LangChain, MCP, tool calling, agent orchestration (not POC/hackathon only)
- RAG implementation depth — vector databases, hybrid retrieval, reranking, knowledge graphs
- AWS Bedrock experience (Azure OpenAI or Vertex AI only acceptable as an alternate, not a substitute for GenAI cloud exposure generally)
- Strong traditional ML/DS background — classification, regression, forecasting, anomaly detection, feature engineering, model evaluation
- 12+ months continuous, recent hands-on GenAI/LLM work — not someone who "recently started exploring" GenAI, not certification-only exposure
- Production deployment experience — has shipped AI/ML systems live, not research/academic-only
- Client-facing/consulting communication skills — can explain architecture decisions and trade-offs to non-technical stakeholders
Please do not submit candidates who:
Have only recent (<12 months) or certification-based exposure to GenAI/LLMs without production deployment
Lack traditional ML/DS depth (i.e., GenAI-only profiles with no classical ML background)
It's a priority for the client. They are also considering candidates based in California now, so you can look for candidates based in either TX or CA.
Must-haves (hard requirements):
Agentic AI PRODUCTION experience - LangGraph, LangChain, MCP, tool calling. No POC/hackathon-only.
12+ months continuous, recent GenAI/LLM work - no "just started exploring" or certification-only.
RAG depth - vector DBs, hybrid retrieval, reranking, knowledge graphs.
AWS Bedrock experience (Azure OpenAI/Vertex AI acceptable alternate, but must be real production use, not just listed as a skill).
Strong traditional ML/DS - classification, regression, forecasting, anomaly detection, feature engineering, model evaluation.
Shipped AI/ML systems to production - no research/academic-only.
Client-facing/consulting communication skills.
Note: Client already rejected a candidate with strong DS/ML skills and good communication because GenAI was POC-level only and had zero Agentic AI experience. Please pre-screen for real, production-level Agentic AI work before submitting.
We are looking for a Senior Data Scientist with a strong traditional ML/DS background and hands-on experience in GenAI (LLMs, RAGs, Agentic workflows). The candidate should not be purely academic or junior; we need someone with practical implementation experience and the ability to interact confidently with customers. Strong communication and product-facing exposure are equally important.
Client is seeking a Senior Data Scientist for our Enterprise Products team. In this role, you will leverage advanced analytical techniques and machine learning methods to derive actionable insights from complex datasets. Your primary responsibilities will include developing predictive models, analyzing data to identify trends, and collaborating with cross-functional teams to implement data-driven solutions that enhance our enterprise product offerings.
Key Responsibilities:
Develop and implement machine learning algorithms to solve complex business problems.
Analyze large datasets to generate insights and inform decision-making processes.
Collaborate with product managers and engineers to integrate data science solutions into enterprise products.
Communicate findings and recommendations effectively to technical and non-technical stakeholders.
Stay current with the latest advancements in data science and machine learning technologies.
Skills and Tools Required:
Strong proficiency in programming languages such as Python or R.
Experience with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
Solid understanding of statistical analysis techniques and data modeling.
Proficiency in data visualization tools (e.g., Tableau, Power BI).
Familiarity with big data technologies (e.g., Hadoop, Spark).
Ability to work with databases and query large datasets using SQL.
Strong problem-solving skills and the ability to think critically.
Excellent communication and collaboration skills.
Preferred Qualifications:
A master's or Ph.D. in computer science, statistics, mathematics, or a related field.
Experience in the tech industry or with enterprise-level software products.
Understanding of cloud computing platforms (e.g., AWS, Azure, Google Cloud).
Roles & Responsibilities
About the Role:
As a Senior Data Scientist in Enterprise Products at Tavant Technologies, you will utilize advanced analytical techniques to drive insights from large datasets. Your role will involve building predictive models and enhancing data-driven decision-making processes within the organization. You will collaborate with various teams to identify opportunities for leveraging data to optimize products and services.
About the Team:
- You will be part of a dynamic team of data scientists, analysts, and product managers dedicated to creating innovative solutions for enterprise-level clients. The team thrives on collaboration, leveraging diverse expertise to tackle complex challenges. A culture of continuous learning and knowledge sharing is fostered, allowing team members to stay up-to-date with the latest industry trends and technologies.
You are Responsible for:
- Developing and deploying machine learning models to solve business problems.
- Analyzing complex datasets to extract actionable insights that contribute to product development.
- Collaborating with cross-functional teams to integrate data science solutions into existing products and services.
- Providing mentorship and guidance to junior data scientists and fostering a collaborative environment.
To succeed in this role – you should have the following:
- Strong experience in machine learning algorithms and statistical modeling techniques.
- Proficiency in programming languages such as Python or R, along with data manipulation libraries.
- Experience with big data technologies like Hadoop, Spark, or similar platforms.
- Excellent analytical skills with the ability to communicate complex findings to non-technical stakeholders.
- A degree in a quantitative field, such as Computer Science, Statistics, Mathematics, or related disciplines.
