AI Data Scientist

Clarios-posted 1 day ago

Full-time • Mid Level

Hybrid • Milwaukee, WI

5,001-10,000 employees

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Clarios is seeking a skilled AI Data Scientist to design, develop, and deploy machine learning and AI solutions that unlock insights, optimize processes, and drive innovation across operations, offices, and products. This role focuses on transforming complex, high-volume data into actionable intelligence and enabling predictive and prescriptive capabilities that deliver measurable business impact. The AI Data Scientist will collaborate closely with AI Product Owners and business SMEs to ensure solutions are robust, scalable, and aligned with enterprise objectives. This role requires an analytical, innovative, and detail-oriented team member with a strong foundation in AI/ML and a passion for solving complex problems. The individual must be highly collaborative, an effective communicator, and committed to continuous learning and improvement. This will be onsite three days a week in Glendale.

Hypothesis Framing & Metric Measurement : Translate business objectives into well-defined AI problem statements with clear success metrics and decision criteria. Prioritize opportunities by ROI, feasibility, risk, and data readiness; define experimental plans and acceptance thresholds to progress solutions from concept to scaled adoption.
Data Analysis & Feature Engineering : Conduct rigorous exploratory data analysis to uncover patterns, anomalies, and relationships across heterogeneous datasets. Apply advanced statistical methods and visualization to generate actionable insights; engineer high-value features (transformations, aggregations, embeddings) and perform preprocessing (normalization, encoding, outlier handling, dimensionality reduction). Establish data quality checks, schemas, and data contracts to ensure trustworthy inputs.
Model Development & Iteration : Design and build models across classical ML and advanced techniques—deep learning, NLP, computer vision, time-series forecasting, anomaly detection, and optimization. Run statistically sound experiments (cross-validation, holdouts, A/B testing), perform hyperparameter tuning and model selection, and balance accuracy, latency, stability, and cost. Extend beyond prediction to prescriptive decision-making (policy, scheduling, setpoint optimization, reinforcement learning), with domain applications such as OEE improvement, predictive maintenance, production process optimization, and digital twin integration in manufacturing contexts.
MLOps & Performance : Develop end-to-end pipelines for ingestion, training, validation, packaging, and deployment using CI/CD, reproducibility, and observability best practices. Implement performance and drift monitoring, automated retraining triggers, rollback strategies, and robust versioning to ensure reliability in dynamic environments. Optimize for scale, latency, and cost; support real-time inference and edge/plant-floor constraints under defined SLAs/SLOs.
Collaboration & Vendor Leadership : Partner with AI Product Owners, business SMEs, IT, and operations teams to translate requirements into pragmatic, integrated solutions aligned with enterprise standards. Engage process owners to validate data sources, constraints, and hypotheses; design human-in-the-loop workflows that drive adoption and continuous feedback. Provide technical oversight of external vendors—evaluating capabilities, directing data scientists/engineers/solution architects, validating architectures and algorithms, and ensuring seamless integration, timely delivery, and measurable value. Mentor peers, set coding/modeling standards, and foster a culture of excellence.
Responsible AI & Knowledge Management : Ensure data integrity, model explainability, fairness, privacy, and regulatory compliance throughout the lifecycle. Establish model risk controls; maintain documentation (model cards, data lineage, decision logs), audit trails, and objective acceptance criteria for production release. Curate reusable assets (feature catalogs, templates, code libraries) and best-practice playbooks to accelerate delivery while enforcing Responsible AI principles and rigorous quality assurance

5+ years of experience in data science and machine learning, delivering production-grade solutions in corporate or manufacturing environments.
Strong proficiency in Python and common data science libraries (e.g., Pandas, NumPy, scikit-learn); experience with deep learning frameworks (TensorFlow, PyTorch) and advanced techniques (NLP, computer vision, time-series forecasting).
Hands-on experience with data preprocessing, feature engineering, and EDA for large, complex datasets.
Expertise in model development, validation, and deployment, including hyperparameter tuning, optimization, and performance monitoring.
Experience interacting with databases and writing SQL queries.
Experience using data visualization techniques for analysis and model explanation.
Familiarity with MLOps best practices—CI/CD pipelines, containerization (Docker), orchestration, model versioning, and drift monitoring.
Knowledge of cloud platforms (e.g., Microsoft Azure, Snowflake) and distributed computing frameworks (e.g., Spark) for scalable AI solutions.
Experience with agile methodologies and collaboration tools (e.g., JIRA, Azure DevOps), working in matrixed environments across IT, analytics, and business teams.
Strong analytical and business acumen, with the ability to quantify ROI and build business cases for AI initiatives.
Excellent communication and stakeholder engagement skills; able to present insights and recommendations to technical and non-technical audiences.

Knowledge of LLMs and VLMs is a strong plus.
Understanding of manufacturing systems (SCADA, PLCs, MES) and the ability to integrate AI models into operational workflows is a strong plus.
Willingness to travel up to 10% as needed.

Medical, dental and vision care coverage and a 401(k) savings plan with company matching – all starting on date of hire
Tuition reimbursement, perks, and discounts
Parental and caregiver leave programs
All the usual benefits such as paid time off, flexible spending, short-and long-term disability, basic life insurance, business travel insurance, Employee Assistance Program, and domestic partner benefits
Global market strength and worldwide market share leadership
HQ location earns LEED certification for sustainability plus a full-service cafeteria and workout facility

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