AI Data Scientist

AI Data Scientist page is loaded## AI Data Scientist locations:
United States, Wisconsin, Milwaukee time type:
Full time posted on:
Posted Todayjob requisition :
WD46598
** What you Will Do
** 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.
** How you will do it**
* ** 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
** What we look for
*** 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, Num Py, scikit-learn); experience with deep learning frameworks (Tensor Flow, 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…