Bennett Data Science

About Faro

Faro is an AI memory layer that allows agentic systems to recall what it learned in the past. Faro currently outperforms industry leader Mem0 on the LoCoMo benchmark (94.1% vs 91.1%). Faro is the memory infrastructure that makes every AI agent more engaging and accurate to work with.

The Role

As an ML/AI researcher at Syntra, you will own the research geared at improving the Faro engine across areas such as: retrieval strategies, memory extraction quality, temporal reasoning, and the evaluation methodology.

You will design benchmarks, build the datasets and LLM judges that score them, run controlled experiments, and turn your research into production ready features. You will play a key role in keeping Faro ahead of its competitors and helping shape the overall product direction.

What you will Work On

• Benchmark datasets, ground truth, and evaluation harnesses
• LLM-judge scoring pipelines with confound controls
• Retrieval, chunking, and memory-extraction experiments
• Accuracy and latency improvements to the core recall engine
• Written recommendations backed by powered, reproducible measurements
• Research code that merges to main in small, production-safe PRs

Must-Haves

• 3+ years of applied ML/NLP research or equivalent industry R&D
• Strong Python capabilities
• LLM pipelines: prompt design, structured extraction, LLM-as-judge evaluation
• Embeddings + vector search (Qdrant, pgvector, Pinecone, or similar)
• Benchmark design: dataset construction, ground truth, statistically powered comparisons
• Engineering discipline: pytest, CI, code review, small reviewable PRs
• Self-starter or experienced with "startup pace"

Nice-to-Haves

• Memory-systems or RAG research (contextual retrieval, temporal reasoning, consolidation)
• Multi-provider LLM experience (OpenAI, AWS Bedrock, Cohere embeddings)
• Agent framework experience (LangChain, LlamaIndex, CrewAI, AutoGen)
• Publications or open-source evaluation and benchmark contributions
• Early-stage startup experience (Series A or earlier)

We will reach out and hold interviews on the week of September 7th, 2026. Thank you.

Who We Are

At Bennett Data Science, we've been pioneering the use of predictive analytics and data science for over a decade for some of the biggest brands and retailers. We're at the top of our field because we focus on delivering actionable AI for our clients. Our deep experience and product-first attitude set us apart from other groups and gets us the business results our clients want.

Why You Should Work With Us

You'll be exposed to a wide range of clients who are at the cutting edge of innovation in their field and get to work on fascinating problems, supporting real products, with real data. We help lots of companies, from some of the largest companies in the world to small startups in Silicon Valley who are building the next big thing.

Expert Mentorship: Direct guidance from senior staff with 20+ years of applied ML experience

Competitive Compensation: Market-rate pay with performance upside

Fully Remote: Work from any location of your choice, on a flexible schedule

The Role:
We are seeking a Senior Data Scientist to support the design, development, validation, and productionization of an advanced forecasting solution. This is a hands-on role requiring strong statistical judgment, practical modeling experience, and the ability to translate analytical findings into clear recommendations.

The successful candidate will work closely with data science, data engineering, and business stakeholders throughout the full modeling lifecycle, from exploratory analysis and model development to deployment, documentation, and handoff.

Key Responsibilities

• Develop, evaluate, and improve forecasting models using Python.
• Establish appropriate baselines and compare alternative modeling approaches.
• Design rigorous time-based backtesting and model-validation frameworks.
• Develop methods for handling limited historical data, changing patterns, and structural shifts.
• Produce prediction intervals and other measures of forecast uncertainty.
• Identify, test, and monitor potential forecast drivers.
• Develop configurable scenario-planning and sensitivity-analysis capabilities.
• Create model diagnostics and early-warning indicators.
• Write modular, tested, maintainable, and well-documented code.
• Collaborate with data engineering on production pipelines, model tracking, and automated forecast runs.
• Explain model results, assumptions, limitations, and changes to technical and non-technical stakeholders.
• Support technical documentation, knowledge transfer, and final solution handoff.
  
  Required Qualifications
• Five or more years of professional experience in data science, applied statistics, econometrics, forecasting, or a related field.
• Strong practical experience developing forecasting or predictive-modeling solutions.
• Advanced Python and SQL skills.
• Strong knowledge of regression, time-series methods, feature engineering, residual analysis, and uncertainty estimation.
• Experience designing rolling or time-based backtests and preventing data leakage.
• Ability to distinguish meaningful predictive signals from unstable or spurious correlations.
• Experience developing production-quality analytical code using version control and standard software-development practices.
• Strong analytical communication and stakeholder-management skills.

• Ability to work independently and deliver within a defined project timeline.
   

  Preferred Qualifications

• Experience developing scenario-planning or decision-support tools.
• Experience working with small historical samples or significant changes in underlying data patterns.
• Familiarity with Microsoft Fabric, Azure, MLflow, or comparable cloud-based analytics platforms.
• Experience productionizing forecasting models and implementing ongoing performance monitoring.
• Experience presenting analytical findings to Finance or senior leadership.