Principal Engineer - Data Analytics III

Job-ID Reference
26-21535

Salary $190,000-$200,000 / year Remote
50% Remote Join a Global Leader in Workforce Solutions – Net2

Source Inc.

Who We Are
Net2

Source Inc. isn't just another staffing company, we're a powerhouse of innovation, connecting top talent with the right opportunities. Recognized for 300% growth in the past three years, we operate in 34 countries with a global team of 5,500+. Our mission? To bridge the talent gap with precision—Right Talent. Right Time. Right Place. Right Price.

Job Title:

Data Scientist

Location:

Oshkosh, Wisconsin, or New Hudson, Michigan (HYBRID - 3 days onsite and 2 remote)
Direct Hire + Benefits + 10% Bonus Interview process:
Teams screening interview with HRBP
Teams interview with HM
Onsite interview with HRBP, HM and management.

Core requirements:
5+ years of experience; 6 years may be appropriate, according to the manager.
AI-savvy and business-savvy, with strong business relationships and experience working directly with business stakeholders—not solely back-end development.
Ability to manage work end to end and personally deploy and deliver the solution.
Shared considerations
Both roles are individual-contributor positions, not manager roles.
The manager is open to candidates who began in a business function or as a business analyst and later moved into IT, analytics, or data science, as well as candidates from consulting.
The roles are described as hybrid: 3 days onsite and 2 remote, based in Oshkosh, Wisconsin, or New Hudson, Michigan. Relocation is acceptable if the candidate is willing to relocate; the manager said these are not commuting positions.
Mentoring and executive-level communication are more central to the Principal role than to the Lead role.

SUMMARY
The Data Science Lead serves at the vital intersection of enterprise business strategy, machine learning innovation, and technical delivery. In this role, you will lead the strategic vision, architectural design, and operational deployment of predictive modeling, advanced analytics, and artificial intelligence solutions across key business units.

Technical Leadership & Solution Architecture
End-to-End Delivery:
Architect, build, and deploy production-grade machine learning models, statistical frameworks, and Generative AI/LLM applications (e.g., RAG architectures, intelligent agents, and fine-tuned models).
Engineering Standards:
Establish best-in-class coding, testing, evaluation, and documentation standards across the team. Conduct regular code and architecture reviews.
Production MLOps:
Partner closely with Data Engineering and Dev Ops teams to establish robust CI/CD pipelines, automated model monitoring, feature stores, data drift detection, and automated retraining workflows.
Research &

Innovation: Stay abreast of state-of-the-art developments in AI/ML, evaluating emerging methodologies, tools, and frameworks to accelerate organizational capabilities.

Strategy, Road mapping & Stakeholder Management
Business Translation:
Collaborate closely with executive leadership, product managers, operations, and commercial leaders to translate complex business challenges into actionable data science initiatives.
Strategic

Roadmap:

Define and prioritize the enterprise AI and analytics roadmap, aligning project timelines, milestones, and deliverables with core business objectives.
Value Realization & Communication:
Articulate complex algorithmic methodologies and quantitative findings in clear, non-technical language to executive stakeholders, driving adoption and demonstrable business impact.
Governance & Ethical AI:
Champion data integrity, security, model explainability, algorithmic fairness, and compliance with organizational policies and international AI safety standards.

People Leadership & Team Development
Mentorship &

Coaching:

Lead, coach, and support junior and mid-level data scientists, nurturing individual career trajectories, continuous learning, and technical mastery.
Agile Project Execution:
Foster an agile, delivery-focused sprint cadence, balancing exploratory research with strict milestone deadlines and business commitments.
Culture & Community:
Cultivate an inclusive, collaborative culture that celebrates technical curiosity, innovation, and knowledge-sharing across cross-functional units.

Education & Professional Experience
Master's degree or Ph.D. in Data Science, Computer Science, Statistics, Mathematics, Operations Research, or a related quantitative discipline (or equivalent practical experience).
Minimum of 6+ years of professional…