Asks for

The ad says“Good understanding of ML techniques for supervised/unsupervised learning, feature selection, dimensionality reduction, regression, classification, clustering, and time-series analysis.”
The ad says“Good understanding of ML techniques for supervised/unsupervised learning, feature selection, dimensionality reduction, regression, classification, clustering, and time-series analysis.”
The ad says“Good understanding of probability, statistics (hypothesis testing, design of experiments, power calculations, mixed-effect models), linear algebra, and the mathematical bases of ML methods.”
The ad says“Good understanding of ML techniques for supervised/unsupervised learning, feature selection, dimensionality reduction, regression, classification, clustering, and time-series analysis.”
The ad says“Good understanding of ML techniques for supervised/unsupervised learning, feature selection, dimensionality reduction, regression, classification, clustering, and time-series analysis.”
The ad says“Good understanding of probability, statistics (hypothesis testing, design of experiments, power calculations, mixed-effect models), linear algebra, and the mathematical bases of ML methods.”
The ad says“Proficiency programming with Python, Julia, or R and ML/statistics packages such as Scikit-Learn, SciPy, Statsmodels, PyTorch, Keras.”
The ad says“Proficiency programming with Python, Julia, or R and ML/statistics packages such as Scikit-Learn, SciPy, Statsmodels, PyTorch, Keras.”
The ad says“Good understanding of probability, statistics (hypothesis testing, design of experiments, power calculations, mixed-effect models), linear algebra, and the mathematical bases of ML methods.”
The ad says“Good understanding of probability, statistics (hypothesis testing, design of experiments, power calculations, mixed-effect models), linear algebra, and the mathematical bases of ML methods.”
The ad says“Good understanding of probability, statistics (hypothesis testing, design of experiments, power calculations, mixed-effect models), linear algebra, and the mathematical bases of ML methods.”
The ad says“Experience with Power BI or Tableau.”
The ad says“Good understanding of probability, statistics (hypothesis testing, design of experiments, power calculations, mixed-effect models), linear algebra, and the mathematical bases of ML methods.”
The ad says“Good understanding of probability, statistics (hypothesis testing, design of experiments, power calculations, mixed-effect models), linear algebra, and the mathematical bases of ML methods.”
The ad says“Proficiency programming with Python, Julia, or R and ML/statistics packages such as Scikit-Learn, SciPy, Statsmodels, PyTorch, Keras.”
The ad says“Proficiency programming with Python, Julia, or R and ML/statistics packages such as Scikit-Learn, SciPy, Statsmodels, PyTorch, Keras.”
The ad says“Proficiency programming with Python, Julia, or R and ML/statistics packages such as Scikit-Learn, SciPy, Statsmodels, PyTorch, Keras.”
The ad says“Good understanding of ML techniques for supervised/unsupervised learning, feature selection, dimensionality reduction, regression, classification, clustering, and time-series analysis.”
The ad says“Proficiency programming with Python, Julia, or R and ML/statistics packages such as Scikit-Learn, SciPy, Statsmodels, PyTorch, Keras.”
The ad says“Proficiency programming with Python, Julia, or R and ML/statistics packages such as Scikit-Learn, SciPy, Statsmodels, PyTorch, Keras.”
The ad says“Experience interacting with databases and writing SQL queries.”
The ad says“Good understanding of probability, statistics (hypothesis testing, design of experiments, power calculations, mixed-effect models), linear algebra, and the mathematical bases of ML methods.”
The ad says“Proficiency programming with Python, Julia, or R and ML/statistics packages such as Scikit-Learn, SciPy, Statsmodels, PyTorch, Keras.”
The ad says“Good understanding of ML techniques for supervised/unsupervised learning, feature selection, dimensionality reduction, regression, classification, clustering, and time-series analysis.”
The ad says“Experience with Power BI or Tableau.”
The ad says“Good understanding of ML techniques for supervised/unsupervised learning, feature selection, dimensionality reduction, regression, classification, clustering, and time-series analysis.”

Values

The ad says“Experience with cloud technologies such as Microsoft Azure or AWS.”
The ad says“Causal inference, probabilistic graphical models, Bayesian statistics, and probabilistic programming languages/packages (Stan, PyMC, PyStan).”
The ad says“Causal inference, probabilistic graphical models, Bayesian statistics, and probabilistic programming languages/packages (Stan, PyMC, PyStan).”
The ad says“Experience with big data technologies such as Databricks, Spark, Snowpark.”
The ad says“Experience with different deep learning architectures and how each applies to different problems.”
The ad says“Experience deploying ML solutions for production (e.g., REST APIs in Azure ML, Docker, Kubernetes) in large data-science projects.”
The ad says“Experience deploying ML solutions for production (e.g., REST APIs in Azure ML, Docker, Kubernetes) in large data-science projects.”
The ad says“Experience mentoring analysts and/or data scientists.”
The ad says“Experience with cloud technologies such as Microsoft Azure or AWS.”
The ad says“Causal inference, probabilistic graphical models, Bayesian statistics, and probabilistic programming languages/packages (Stan, PyMC, PyStan).”
The ad says“Causal inference, probabilistic graphical models, Bayesian statistics, and probabilistic programming languages/packages (Stan, PyMC, PyStan).”
The ad says“Causal inference, probabilistic graphical models, Bayesian statistics, and probabilistic programming languages/packages (Stan, PyMC, PyStan).”
The ad says“Experience deploying ML solutions for production (e.g., REST APIs in Azure ML, Docker, Kubernetes) in large data-science projects.”
The ad says“Experience with big data technologies such as Databricks, Spark, Snowpark.”
The ad says“Experience with big data technologies such as Databricks, Spark, Snowpark.”
The ad says“Causal inference, probabilistic graphical models, Bayesian statistics, and probabilistic programming languages/packages (Stan, PyMC, PyStan).”

The Role

Join the Connected Services Business Unit as a Data Scientist developing battery-health algorithms fed by real-time vehicle data, using state-of-the-art statistical and ML techniques. You will also strengthen core manufacturing and quality processes using machine-level data from our plants — designing systems that monitor data streams, send signals and alerts to customers, and add ML pipelines that make existing processes smarter.

Connected Services develops connected solutions to reduce or eliminate no-start failures and enable in-use data — powered by sensors, gateways, and IoT — delivered to end users via APIs, dashboards, portals, and mobile apps, targeting Heavy Duty & Auto for Fleets, Aftermarket, and OE.

What You’ll Do (Impact Areas)

Decode signals: build data-analysis pipelines to understand vehicle signals related to battery usage across applications, and machine signals related to battery manufacturing processes.
Build battery-health intelligence: develop, validate, and maintain models that provide battery-health assessments to customers of our connected battery products.
Optimize manufacturing: build and maintain models to increase production throughput, reduce scrap rates, and improve product quality.
Collaborate & translate: work with data scientists, engineers, and business stakeholders to craft solutions, and communicate the data-driven decision process to non-technical stakeholders.
Support broadly: provide ML and statistical solutions to other areas of the company as needed.

What Success Looks Like

Reliable battery-health models in production, giving connected-product customers assessments they can trust.
Measurable manufacturing gains: higher throughput, lower scrap rates, and improved product quality driven by machine-level data.
Monitoring systems that surface meaningful signals and timely alerts to customers.
Clear, well-communicated insights that non-technical stakeholders can act on.

Core Competencies

Strong statistical and ML foundations: hypothesis testing, design of experiments, regression, classification, clustering, and time-series analysis.
Hands-on model development, training, validation, and deployment.
Data storytelling and visualization for both analysis and model explanation.
Curiosity, creativity, and self-direction in a dynamic business environment.
Effective collaboration across diverse, cross-functional teams.

What You Bring (Qualifications)

Required

BS in Statistics, Mathematics, Computer Science, or a related engineering field.
3+ years of experience, or equivalent academic experience with a master’s/PhD program in a relevant field.
Proficiency programming with Python, Julia, or R and ML/statistics packages such as Scikit-Learn, SciPy, Statsmodels, PyTorch, Keras.
Experience with Power BI or Tableau.
Good understanding of probability, statistics (hypothesis testing, design of experiments, power calculations, mixed-effect models), linear algebra, and the mathematical bases of ML methods.
Good understanding of ML techniques for supervised/unsupervised learning, feature selection, dimensionality reduction, regression, classification, clustering, and time-series analysis.
Experience interacting with databases and writing SQL queries.
Experience developing, training, validating, and deploying ML/statistical models.
Experience using data-visualization techniques for analysis and model explanation.
Experience collaborating effectively with non-technical stakeholders; self-driven, curious, and creative.

Preferred

Experience with different deep learning architectures and how each applies to different problems.
Experience with big data technologies such as Databricks, Spark, Snowpark.
Experience with cloud technologies such as Microsoft Azure or AWS.
Experience deploying ML solutions for production (e.g., REST APIs in Azure ML, Docker, Kubernetes) in large data-science projects.
Experience mentoring analysts and/or data scientists.
Causal inference, probabilistic graphical models, Bayesian statistics, and probabilistic programming languages/packages (Stan, PyMC, PyStan).
Experience in manufacturing, IoT, vehicle data, and/or battery engineering.
Publications in peer-reviewed journals and/or conferences in statistics, mathematics, or machine learning.

About Clarios:

Clarios is the global leader in advanced, low-voltage battery technologies for mobility. Our batteries and smart solutions power nearly every type of vehicle and are found in 1 of 3 cars on the road today. With around 18,000 employees in over 100 countries, we bring deep expertise to our Aftermarket and OEM partners, and reliability, safety and comfort to everyday lives. We answer to the planet with a rigorous sustainability focus – advancing best-in-class sustainability practices and advocating for them across our industry. We work to ensure 100% of our products sold are recyclable, and we recycle 8,000 batteries an hour in our network. You can find more information here (PDF).

To All Recruitment Agencies: Clarios does not accept unsolicited agency resumes/CVs. Please do not forward resumes/CVs to our careers email addresses, Clarios employees or any other company location. Clarios is not responsible for any fees related to unsolicited resumes/CVs.

Equal Employment Opportunity:
Clarios, LLC is an equal employment opportunity and affirmative action employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, age, protected veteran status, status as a qualified individual with a disability, or any other characteristic protected by law. For more information, please view EEO is the Law, EEO is the Law (supplement), and Pay Transparency Non-discrimination. If you are an individual with a disability and you require an accommodation during the application process, please email Special.Accommodations@Clarios.com.

A Note to Job Applicants: please be aware of scams being perpetrated through the Internet and social media platforms. Clarios will never require a job applicant to pay money as part of the application or hiring process.