Lead Data Scientist Applied AI - USA

Cogniify•Santa Clara, CA

1d•$150,000 - $170,000•Remote

About The Position

We are seeking a Lead Data Scientist who can build advanced AI systems and demonstrate, with evidence, why they are accurate, reliable and appropriate for production. This role combines strong statistical and machine learning expertise with hands-on experience designing and delivering production AI solutions. You will work across Generative AI, Agentic AI, computer vision, forecasting and optimization. Your central responsibility will be to measure model performance and uncertainty, explain model behavior, identify risks and failure modes, and connect technical results to business outcomes that leaders can use to make decisions. The ideal candidate is comfortable running structured experiments, presenting error analysis to technical and non-technical stakeholders, and taking models from problem definition through deployment and monitoring. You should know when Generative AI is the right solution, when a simpler statistical approach is more effective, and how to support that decision with data.

Requirements

Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, Statistics or a related discipline, or equivalent practical experience.
A track record of approximately 10 or more AI and machine learning projects deployed to production, with clear ownership of approach selection, evaluation and risk assessment.
Strong statistical and algorithmic foundations, including hypothesis testing, experiment design, evaluation methodology and uncertainty quantification.
Hands-on experience with RAG, tool-use patterns and agentic frameworks such as LangGraph, LlamaIndex, AutoGen or CrewAI, along with Model Context Protocol where relevant.
Experience evaluating and protecting LLM and agentic systems through guardrails, systematic testing, hallucination measurement and failure analysis.
Practical experience with explainability, bias and fairness assessment, model monitoring and drift detection.
Familiarity with MLOps practices, including experiment tracking, CI/CD for machine learning, model registries and production monitoring.
Experience with distributed training or inference optimization, including quantization, batching and GPU utilization.
Working knowledge of Docker and Kubernetes.
Strong communication skills, with the ability to present model results, risks and trade-offs using clear numbers for engineers, business leaders and executives.

Nice To Haves

Deep Learning: PyTorch, TensorFlow, Keras, JAX and PyTorch Lightning.
Generative AI and fine-tuning: Hugging Face Transformers, PEFT, LoRA, QLoRA, TRL, Accelerate, DeepSpeed, bitsandbytes, Axolotl and Unsloth.
Model serving: vLLM, TGI and Ollama.
RAG and orchestration: LangChain and LlamaIndex.
Agentic AI: Claude Agent SDK, Anthropic and OpenAI SDKs, LangGraph, AutoGen, CrewAI, Semantic Kernel, Model Context Protocol, tool calling and multi-agent patterns.
Computer vision: OpenCV, Detectron2, Segment Anything and image or video processing pipelines.
Forecasting and optimization: statsmodels, Prophet, GluonTS, Darts, scikit-learn, OR-Tools, SciPy, PuLP, Gurobi and CVXPY.
Evaluation and explainability: SHAP, LIME, LLM evaluation frameworks, error analysis, slice analysis and A/B testing.
MLOps and infrastructure: MLflow, Weights & Biases, Docker, Kubernetes, CI/CD for machine learning, model registries and monitoring.

Responsibilities

Justify modeling decisions with evidence: Frame business problems, form hypotheses, run structured experiments and select between classical machine learning, deep learning and Generative AI using measured performance, cost and risk.
Quantify risk and uncertainty: Measure confidence intervals, error rates, hallucination rates, bias, drift and failure modes. Define numerical production-readiness criteria for each use case.
Improve explainability: Use feature attribution, SHAP, LIME, error analysis, slice analysis and evaluation dashboards to explain model behavior and limitations.
Translate results into business impact: Connect model metrics to revenue, cost, speed and risk. Quantify expected return, trade-offs and downside scenarios for decision-makers.
Design complete AI solutions: Own problem framing, data strategy, modeling, evaluation, deployment and monitoring for machine learning, LLM and agentic systems.
Build models for unstructured data: Develop production-quality solutions using image, video, text, audio, sensor and time-series data.
Deliver computer vision solutions: Build detection, classification, segmentation, OCR and tracking systems with measurable performance benchmarks.
Develop forecasting solutions: Create time-series, demand and behavioral forecasting models with documented accuracy, error bands and integration into operational workflows.
Build with foundation models: Use Claude and other LLMs for prompting, fine-tuning, systematic evaluation and integration into multi-step, tool-using and multi-agent workflows with memory and guardrails.

Benefits

Unlimited paid time off.
Generous parental leave that exceeds typical industry standards.
An entrepreneurial culture that supports thoughtful experimentation and calculated risk-taking.
Open communication with management and company leadership.
Small, dynamic teams where individual contributions have visible impact.
Medical, dental and vision coverage for employees.
Access to disability and life insurance.
Mental health and wellbeing support.
Annual bonus program.
Employer Stock Purchase Program.
Annual team-building experiences.
Mentorship and sponsorship opportunities.
Resources and support for people managers.