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Data Scientist ​/ Machine Learning Engineer (Gen AI Focus) (contract

Job in Charlotte, Mecklenburg County, North Carolina, 28245, USA
Listing for: Socket.dev
Full Time, Contract position
Listed on 2026-08-08
Job specializations:
  • IT/Tech
    Machine Learning/ ML Engineer, Data Scientist, AI Engineer (Applied/Software), Data Analyst
Salary/Wage Range or Industry Benchmark: 110000 - 160000 USD Yearly USD 110000.00 160000.00 YEAR
Job Description & How to Apply Below
Position: Data Scientist / Machine Learning Engineer (Gen AI Focus) (contract)

Title:Data Scientist / Machine Learning Engineer (Gen AI Focus)

Location: Charlotte, NC

Duration: 12 months

Work Engagement: W2

Work Schedule
:
Hybrid 3 days in office/2 days remote

Benefits on offer for this contract position
:
Health Insurance, Life insurance, 401K and Voluntary Benefits

Summary:

In this contingent resource assignment, you may:
Consult on complex initiatives with broad impact and large-scale planning for Software Engineering. Review and analyze complex multi-faceted, larger scale or longer-term Software Engineering challenges that require in-depth evaluation of multiple factors including intangibles or unprecedented factors. Contribute to the resolution of complex and multi-faceted situations requiring solid understanding of the function, policies, procedures, and compliance requirements that meet deliverables.

Strategically collaborate and consult with client personnel.

Required Qualifications:

Software Engineering experience, or equivalent demonstrated through one or a combination of the following: work or consulting experience, training, military experience, education.

Key Responsibilities:
  • The candidate will perform in-depth data analysis and exploration using SQL and statistical techniques to uncover patterns, solve business problems, and support data-driven decision-making. This includes working with large, complex datasets and ensuring data quality, integrity, and usability.
  • They will design, develop, and implement scalable solutions using Python or Java, leveraging standard data science and machine learning libraries such as Num Py, Sci Py, Matplotlib, and Scikit-learn. Responsibilities include building reusable pipelines for data processing, feature engineering, and model evaluation.
  • The role involves developing and evaluating machine learning models, including tree-based and ensemble algorithms such as Random Forest and XGBoost. The candidate will assess model performance, tune hyperparameters, and ensure models meet business and technical requirements.
  • A key component of this position is applying AI-assisted techniques to enhance productivity and insights. This includes crafting effective prompts using Gemini or similar generative AI models to accelerate tasks such as data exploration, feature generation, analysis, and summarization of findings.
  • The candidate will communicate insights clearly through visualizations, reports, and presentations, translating complex technical outputs into actionable business recommendations. They will partner closely with engineering teams for implementation and with business stakeholders to ensure alignment with strategic goals.
Key Requirements:
  • Applicants must be authorized to work for ANY employer in the U.S. This position is not eligible for visa sponsorship.
  • Candidates must possess strong SQL and data analysis skills, with the ability to work efficiently across structured and semi-structured datasets. Proficiency in Python or Java is required, particularly for data science, machine learning, and analytical workloads.
  • Hands-on experience with machine learning frameworks and model development is essential, including experience building, training, and evaluating predictive models in production or near-production environments.
  • The candidate should demonstrate the ability to work independently and own initiatives end-to-end, from problem definition and requirements gathering through solution delivery and validation.
  • Experience using generative AI models to augment analytical workflows is required. This includes familiarity with prompt engineering, leveraging LLMs for automation, and integrating Gen AI capabilities into analytical processes.
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