- Company Name
- Mendo
- Job Title
- Senior Data Scientist / AI Engineer
- Job Description
-
Job Title
Senior Data Scientist / AI Engineer
Role Summary
Lead the end‑to‑end development and deployment of AI/ML and GenAI solutions that drive measurable product value for enterprise users. Own research, modeling, production pipelines, and continuous improvement while mentoring junior data scientists and setting data‑science standards.
Expectations
- Deliver high‑impact AI features aligned with product roadmap that demonstrate clear ROI.
- Demonstrate end‑to‑end ownership from research to production monitoring and cost‑efficiency.
- Mentor and develop a junior data scientist, establishing best‑practice guidelines for the team.
Key Responsibilities
1. Identify AI opportunities by analyzing product usage, customer feedback, and business objectives.
2. Prioritize and scope AI initiatives for maximum user impact and ROI.
3. Research, prototype, and fine‑tune LLMs and GenAI architectures (RAG, embeddings, semantic search).
4. Design and implement end‑to‑end AI pipelines: data prep, training/fine‑tuning, evaluation, deployment.
5. Optimize model performance and operational costs (latency, token usage, infrastructure).
6. Implement monitoring, A/B testing, and continuous quality evaluation of models in production.
7. Document technical decisions, trade‑offs, and standards for reproducibility.
8. Mentor junior data scientists and promote a culture of experimentation and rapid iteration.
9. Collaborate with product, engineering, and customer success to integrate models and validate impact.
10. Present results and strategic recommendations to product, sales, and leadership stakeholders.
Required Skills
- 4–6+ years in ML/AI research, development, and deployment.
- Deep expertise in LLM fine‑tuning (OpenAI, Anthropic, Llama, Mistral, etc.).
- Proficiency with GenAI architectures: RAG, embeddings, prompt engineering, semantic search.
- Strong command of ML/DL frameworks: PyTorch, TensorFlow, Hugging Face Transformers.
- Experience with LangChain, LlamaIndex, and vector databases (Pinecone, Weaviate, Qdrant).
- Familiarity with MLOps: model versioning, experimentation, A/B testing, deployment pipelines.
- Cloud ML platform experience: Azure ML, AWS SageMaker, or Google Vertex AI.
- Mastery of Python and data‑science libraries (scikit‑learn, pandas, numpy).
- Ability to evaluate models rigorously, benchmark metrics, and iterate based on user feedback.
- Excellent communication skills for translating technical concepts to non‑technical audiences.
- Proven ability to prioritize, innovate with pragmatism, and deliver value quickly.
Required Education & Certifications
- Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related quantitative field.
- Relevant AI/ML certifications (e.g., Google Cloud ML, AWS Machine Learning Specialty, or equivalent) are a plus.