Senior Data Scientist, AI Strategy

Intuitive.ai; United States; Remote; USD 130,000 - 180,000; Full time

Job summary

Intuitive.AI is seeking a Senior Data Scientist, AI Strategy to define data strategy and lead end-to-end data science workstreams for AI use cases. This remote role across the USA ensures designs are fully executable and production-ready before deployment.

The role emphasizes shaping the approach, evaluating LLMs/agents, and guiding experiments, with strong emphasis on MLOps and stakeholder alignment to deliver scalable AI solutions.

Qualifications

• 7+ years of hands-on data science experience with methodology design on complex AI projects
• Proven experience evaluating LLM and agentic systems beyond accuracy and building ground truth test sets
• Strong grounding in experiment design, statistics and setting acceptance thresholds in advance
• Deep hands-on experience with classic ML (classification, regression, time series) and GenAI including RAG and prompt engineering
• Practical understanding of MLOps and production deployment
• Ability to shape scope with stakeholders and present data-driven recommendations at gates

Responsibilities

• Translate business requirements into clearly defined AI and GenAI use cases and task types
• Define baseline and business metrics to measure AI solution impact
• Establish a curated evaluation dataset with labeling plan and ground truth
• Develop and execute EDA strategy covering data quality and leakage
• Create DOE for testing approaches and establish evaluation criteria
• Lead and own data science deliverables and artifacts for readiness and approval
• Ensure production-ready designs and act as the contact for data/model issues during implementation and after go-live

Skills

Data science experience

LLM & agent evaluation

Experiment design & statistics

Machine learning & GenAI

Production MLOps awareness

Stakeholder leadership

Education

Master’s degree or PhD in a quantitative field

Tools

LangChain

LangGraph

Job description

Intuitive.AI is one of the fastest-growing (INC 5000, CRN) Cloud & SDx solution and services companies supporting enterprise customers on a global scale. Intuitive is an "Engineering Company" delivering measurable value and key business outcomes.

Intuitive Superpowers:

- DataOps & AI/ML

- Cybersecurity (App/Data/Infra) & GRC

- SDx & Digital Workspace

We are proud to partner with some of the world's leading enterprises and serve 200+ customers across different industry verticals. We have achieved many milestones along the way, including being recognized as a top-10 fast-growth 150 IT company in the Americas by CRN in 2022 and being named one of America's fastest-growing private companies by INC 5000 in 2022. That’s not all! Even "CIO Review" awarded us as the Most Promising Cloud Migration Company and Artificial Intelligence Solutions Provider in 2022.

About the job:

Title: Senior Data Scientist, AI Strategy

Location: Remote across USA

Position Type: Full Time

Position Overview

Role Summary:

The customer AI Strategy team designs the methodology, data strategy and experiments behind each AI use case, then hands a validated design to a separate delivery team to build. The team remains accountable for that design after deployment and go-live, so the work has to be fully executable and hold up in production.

This is not primarily an execution role. The Senior Data Scientist defines the approach, shapes the solution, and establishes the data science strategy that guides implementation and long term sustainability. Each person leads the data science workstream for their use cases.

Key Responsibilities:

• Use case definition: Translate business requirements into clearly defined AI and GenAI use cases and task types, such as classification, extraction, summarization, retrieval augmented generation (RAG) and recommendation. Judge where an agent adds value and where traditional machine learning is the better fit.
• Baseline and success metrics: Define the baseline business and operational metrics the AI solution is expected to improve, and connect model metrics directly to those business outcomes before any build begins.
• Evaluation dataset: Establish a curated evaluation dataset before implementation, including edge cases and adversarial examples, with a clear labeling approach and ground truth.
• Exploratory data analysis: Develop and execute the EDA strategy and sampling approach, covering data quality, representativeness and early detection of data leakage. Analyze the findings and recommend how they should guide solution design.
• Design of experiments: Create the design of experiments (DOE), the formal plan for testing which approach works best, including hypotheses, factors and levels, experiment metrics, evaluation methodology and decision criteria agreed before the experiment runs.
• TC2 ownership: Lead and own all data science deliverables and artifacts required for readiness and approval.
• TC3 accountability: Serve as the accountable data science lead through TC3, validating proof of concept execution against the approved DOE, evaluation criteria and acceptance thresholds, and providing the technical recommendation on whether to progress to a minimum viable product (MVP).
• Production readiness: Ensure designs handed to the delivery team are fully executable and will not break in production, and act as the point of contact for data or model issues during implementation and after go-live.

Required Qualifications:

• Experience: 7 or more years of hands-on data science experience, including personal ownership of methodology design on complex AI projects from scoping through delivery.
• LLM and agent evaluation: Proven experience evaluating LLM and agentic systems beyond accuracy, including building ground truth test sets and measuring routing and tool call correctness, groundedness, faithfulness, completeness, hallucination rates and end to end task success.
• Experiment design and statistics: Strong grounding in experiment design and statistics, including comparing models or prompt versions on small labeled datasets, accounting for run to run variability, and setting acceptance thresholds in advance.
• Machine learning and GenAI depth: Deep hands-on experience with classic machine learning (classification, regression, time series) and with GenAI, including RAG, agent frameworks such as LangChain or LangGraph, and prompt engineering.
• Production awareness: Practical understanding of MLOps and production deployment, sufficient to design solutions that a separate engineering team can implement reliably.
• Stakeholder leadership: Demonstrated ability to shape scope with business stakeholders, push back with data, explain design trade-offs, and present recommendations at approval gates without needing close direction.

Preferred Qualifications:

• Industrial domain: Experience in energy, power generation, manufacturing or other industrial settings, particularly with sensor, time series or maintenance data.
• Advanced degree: A master’s degree or PhD in data science, statistics, applied mathematics or a related quantitative field.
• Mentoring: Experience guiding or mentoring other data scientists on methodology and evaluation practice