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Data Scientist Ai Strategy W Contract

  • Data Scientist Ai Strategy W Contract job type Posted on: Oct 14, 2026
  • Data Scientist Ai Strategy W Contract experience level VeeRteq Solutions Inc
  • Data Scientist Ai Strategy W Contract employment type New York, New York
  • Data Scientist Ai Strategy W Contract employment type Remote
  • Data Scientist Ai Strategy W Contract salary Full-time
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Job Title :

Data Scientist Ai Strategy W Contract

Job Type :

Full-Time

Job Location :

New York New York United States

Remote :

Yes

Jobcon Logo Job Description :

Role: Sr. Data Scientist - AI Strategy
Location: Remote (EST)
Duration: Long Term
Contract Type: W2
Role Summary:
The 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 from L2 scoping through TC3, two milestones in Client's AI review process that are explained in the next section. They operate independently and partner closely with business stakeholders across several use cases running in parallel.
How Use Cases Move Through Client Review Gates:
Client moves every AI use case through a series of formal review gates run by its Technical Committee (TC). Each gate is a decision point where the work so far is reviewed and the use case is approved to move forward, sent back for rework, or stopped. This role owns the data science work for the stages shown below.
TC1: Use case approval
The business proposes a use case and it is reviewed for strategic fit, value and feasibility. This happens before the use case reaches this role.
L2 scoping: Handover
A formal handover meeting where the business team passes an approved use case to the AI Strategy team, along with its success metrics, data sources, key stakeholders and scope boundaries. This is where this role's ownership begins.
TC2: Design approval
The data science plan is reviewed and approved before any proof of concept is built. This role prepares and owns the material for this gate: the use case definition, baseline metrics, evaluation dataset, exploratory data analysis findings and the experiment design.
TC3: Proof of concept validation
The proof of concept results are checked against the experiment design, evaluation criteria and acceptance thresholds approved at TC2. This role is the accountable data science lead for this gate and recommends whether the use case should progress to a minimum viable product (MVP).
TC4 and beyond: Production
Final approval before production and handover to operations, led by the delivery team. This role stays the point of contact for data and model questions.
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 TC2 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.

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Jobcon Logo Position Details

Posted:

Oct 14, 2026

Reference Number:

4618-15867

Employment:

Full-time

City:

New York

Job Origin:

CIEPAL_ORGANIC_FEED

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Role: Sr. Data Scientist - AI Strategy
Location: Remote (EST)
Duration: Long Term
Contract Type: W2
Role Summary:
The 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 from L2 scoping through TC3, two milestones in Client's AI review process that are explained in the next section. They operate independently and partner closely with business stakeholders across several use cases running in parallel.
How Use Cases Move Through Client Review Gates:
Client moves every AI use case through a series of formal review gates run by its Technical Committee (TC). Each gate is a decision point where the work so far is reviewed and the use case is approved to move forward, sent back for rework, or stopped. This role owns the data science work for the stages shown below.
TC1: Use case approval
The business proposes a use case and it is reviewed for strategic fit, value and feasibility. This happens before the use case reaches this role.
L2 scoping: Handover
A formal handover meeting where the business team passes an approved use case to the AI Strategy team, along with its success metrics, data sources, key stakeholders and scope boundaries. This is where this role's ownership begins.
TC2: Design approval
The data science plan is reviewed and approved before any proof of concept is built. This role prepares and owns the material for this gate: the use case definition, baseline metrics, evaluation dataset, exploratory data analysis findings and the experiment design.
TC3: Proof of concept validation
The proof of concept results are checked against the experiment design, evaluation criteria and acceptance thresholds approved at TC2. This role is the accountable data science lead for this gate and recommends whether the use case should progress to a minimum viable product (MVP).
TC4 and beyond: Production
Final approval before production and handover to operations, led by the delivery team. This role stays the point of contact for data and model questions.
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 TC2 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.

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