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Digital Technology Data Scientist Lead
Zobility
- πΊπΈ United States
- On-site
- Staff / Principal
- 6 hours ago
- Machine Learning
- AI
- RAG
- MLOps
- Devops
- CI/CD
- AI/ML
- Agile
- Python
- NumPy
- Pandas
- scikit-learn
- PyTorch
- TensorFlow
- SQL
- Natural Language Processing
- AWS
- Azure
- GCP
- Databricks
- Snowflake
- Apache Spark
- Docker
- Kubernetes
- Kubeflow
- Airflow
- Power BI
- Pension
6 hours ago
Job Description:
- 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.
- 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., ML flow, Docker, Kubernetes, Kubeflow, Airflow).
- Proficiency in presenting findings via business intelligence and visualization tools (e.g., Power BI, Stream lit).
- 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.
- Master's degree or Ph.D. in Data Science, Computer Science, Statistics, Mathematics, Operations Research, or a related quantitative discipline (or equivalent practical experience).
We offer attractive, competitive compensation and benefits including, medical, dental, 401k, short-term disability, AD&D, tuition reimbursement, and more. If you take pride in your work and are committed to personal and professional success, let's talk. Please visit www.zobility.com to learn more.
Zobility is RGBSI's workforce management and staffing division. RGBSI is a multi-national corporation headquartered in Troy, MI with branches throughout the USA, Canada, Germany, and India.
Digital Technology Data Scientist Lead Β· Zobility