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Principal Engineer - Data Analytics III
SGA Inc.
- 🇺🇸 United States
- Hybrid
- Staff / Principal
- 15 hours ago
- AI
- Machine Learning
- Python
- SQL
- RAG
- MLOps
- Devops
- CI/CD
- AI/ML
- Agile
- NumPy
- Pandas
- scikit-learn
- PyTorch
- TensorFlow
- Natural Language Processing
- AWS
- Azure
- GCP
- Databricks
- Snowflake
- Apache Spark
- MLflow
- Docker
- Kubernetes
- Kubeflow
- Airflow
- Power BI
- Streamlit
15 hours ago
The Data Science Lead serves as a hands-on technical leader who bridges business strategy, advanced analytics, and AI-driven innovation to deliver high-impact solutions across the organization. This individual contributor role requires 5+ years of experience leading end-to-end data science initiatives, partnering directly with business stakeholders, and translating complex business challenges into scalable machine learning, predictive analytics, and Generative AI solutions. The ideal candidate combines strong technical expertise in Python, SQL, cloud platforms, and modern AI/LLM technologies with the ability to independently architect, deploy, and support production-ready solutions. Data Science Lead collaborates closely with executive leadership, operations, product teams, and IT partners to define AI roadmaps, drive adoption, establish technical standards, and deliver measurable business value through data-driven decision making.
Core requirements
- 5+ years of experience; 6 years may be appropriate , according to the manager.
- The same core profile as R50808: 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.
- 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.
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 DevOps 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.
- 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.
- 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.
- 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 experience in data science, predictive modeling, or machine learning engineering.
- Expert proficiency in Python (including NumPy, Pandas, Scikit-Learn, PyTorch, or TensorFlow) and advanced SQL.
- Strong hands-on experience building supervised/unsupervised learning pipelines, deep learning models, natural language processing (NLP), and modern Large Language Model (LLM) architectures.
- Proven track record utilizing enterprise cloud ecosystems (AWS, Azure, or GCP) and distributed compute systems (Databricks, Snowflake, Apache Spark).
- Demonstrated familiarity with model tracking, containerization, and orchestration platforms (e.g., MLflow, Docker, Kubernetes, Kubeflow, Airflow).
- Proficiency in presenting findings via business intelligence and visualization tools (e.g., Power BI, Streamlit).
- Strategic Thinking: Ability to see the bigger picture, connecting data capabilities to long-term business strategy.
- Outstanding verbal and written communication skills with the capability to influence senior leadership.
- Pragmatism & Value Orientation: Focus on business utility, avoiding over-engineering in favor of reliable, interpretable, and maintainable solutions.
By applying for a job with SGA, you agree to allow SGA to process your application for this and future opportunities in accordance with our Privacy Policy. Also, to ensure timely processing, you agree to be contacted by our AI recruiter via email, text, or phone. Message frequency varies and data rates may apply, but you can reply STOP to any SMS message to opt-out of texts and may contact SGA at info@sgainc.com to opt-out of AI communications. The choice not to engage with AI will not adversely impact your consideration for placement. AI is not used to make any hiring determinations.
SGA is a technology and resource solutions provider driven to stand out. We are a women-owned business. Our mission: to solve big IT problems with a more personal, boutique approach. Each year, we match consultants like you to more than 1,000 engagements. When we say let's work better together, we mean it. You'll join a diverse team built on these core values: customer service, employee development, and quality and integrity in everything we do. Be yourself, love what you do and find your passion at work. Please find us at https://sgainc.com/ .
SGA is an Equal Opportunity Employer and does not discriminate on the basis of Race, Color, Sex, Sexual Orientation, Gender Identity, Religion, National Origin, Disability, Veteran Status, Age, Marital Status, Pregnancy, Genetic Information, or Other Legally Protected Status. We are committed to providing access, equal opportunity, and reasonable accommodation for individuals with disabilities in employment, and our services, programs, and activities. Please visit our company EEO page to request an accommodation or assistance regarding our policy.
Principal Engineer - Data Analytics III · SGA Inc.