Senior Data Scientist

Description

• Contribute to internal knowledge sharing and cross-functional learning through documentation, demos, and collaborative experimentation.
• Stay on top of emerging methods and tools in ML, deep learning, NLP, and agentic workflows—bringing new ideas to the table and pushing the envelope of what’s possible.
• Collaborate with stakeholders across R&D (Product, Engineering, Research, Design) and GTM to frame analytical problems, align on objectives, and define success metrics.
• Translate ambiguous business challenges into clearly scoped, feasible data science projects.
• Partner with Engineering to design and implement AI-powered product features, applying best practices around model selection, system design, optimization, and monitoring.
• Evaluate LLM and generative AI features using both quantitative metrics and qualitative techniques—developing benchmarks, stress tests, and custom evaluation pipelines to ensure reliability and performance.
• Work with Analytics Engineering and Data Engineering teams to source and validate high-quality data for model development.
• Conduct exploratory data analysis (EDA) to surface trends, patterns, outliers, and opportunities for impact.

Requirements

• Experience with LLMs, embeddings, and vector search tools (e.g. LangChain, OpenAI APIs, Pinecone, FAISS).
• Ability to clearly communicate technical concepts to stakeholders across varying levels of technical fluency.
• Comfort using tools like Jupyter, Looker, GitHub, or cloud platforms (AWS, GCP).
• Strong analytical skills and experience working with large datasets using SQL, Pandas, and other data tools.
• 3–5+ years of experience in data science, ML, or applied statistics in a product or growth-oriented B2B SaaS environment.
• Familiarity with agentic AI workflows, prompt engineering, or RAG (Retrieval-Augmented Generation) pipelines.
• Experience building evaluation pipelines for ML and AI models.
• Experience collaborating closely with Product, Engineering, or Marketing teams to ship user-facing AI features or data products.
• Previous experience working in cross-functional environments and driving ML/AI projects from ideation to deployment.
• Demonstrated ability to design, train, and validate models using structured and unstructured data.
• Strong programming skills in Python and experience with ML libraries like scikit-learn, XGBoost, PyTorch, or TensorFlow.

Benefits

Training + Development

Interview process

Visa Sponsorship

Security clearance