{"id":1066737,"url":"https://alion.io/job/clarios-data-scientist","title":"Data Scientist","company":{"id":6276,"name":"Clarios","domain":"clarios.com","url":"https://alion.io/company/clarios","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":78,"open_postings":45,"ghost_share":0,"stale_share":0.889,"repost_share":0,"time_to_fill_p50_days":33,"computed_at":"2026-10-17T05:46:00Z"}},"role":"Data Science","role_family":"Data Science","seniority":"middle","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Monterrey, Mexico"],"countries":["MX"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":25000,"max_usd":69000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":865},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Databricks","optional":false},{"name":"Docker","optional":false},{"name":"Julia","optional":false},{"name":"Keras","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"Power BI","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Rest API","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SciPy","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Statsmodels","optional":false},{"name":"Tableau","optional":false},{"name":"Time Series Forecasting","optional":false},{"name":"TensorFlow","optional":true}],"status":"live","first_seen_at":"2026-06-10T00:00:00Z","employer_posted_date":"2026-06-10","last_verified_at":"2026-10-17T05:10:21Z","board_verified":true,"closed_at":null,"days_open":129,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":129},"description":"The Role\nJoin 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.\nConnected 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.\nWhat You’ll Do (Impact Areas)\nDecode signals: build data-analysis pipelines to understand vehicle signals related to battery usage across applications, and machine signals related to battery manufacturing processes.\nBuild battery-health intelligence: develop, validate, and maintain models that provide battery-health assessments to customers of our connected battery products.\nOptimize manufacturing: build and maintain models to increase production throughput, reduce scrap rates, and improve product quality.\nCollaborate & translate: work with data scientists, engineers, and business stakeholders to craft solutions, and communicate the data-driven decision process to non-technical stakeholders.\nSupport broadly: provide ML and statistical solutions to other areas of the company as needed.\nWhat Success Looks Like\nReliable battery-health models in production, giving connected-product customers assessments they can trust.\nMeasurable manufacturing gains: higher throughput, lower scrap rates, and improved product quality driven by machine-level data.\nMonitoring systems that surface meaningful signals and timely alerts to customers.\nClear, well-communicated insights that non-technical stakeholders can act on.\nCore Competencies\nStrong statistical and ML foundations: hypothesis testing, design of experiments, regression, classification, clustering, and time-series analysis.\nHands-on model development, training, validation, and deployment.\nData storytelling and visualization for both analysis and model explanation.\nCuriosity, creativity, and self-direction in a dynamic business environment.\nEffective collaboration across diverse, cross-functional teams.\nWhat You Bring (Qualifications)\nRequired\nBS in Statistics, Mathematics, Computer Science, or a related engineering field.\n3+ years of experience, or equivalent academic experience with a master’s/PhD program in a relevant field.\nProficiency programming with Python, Julia, or R and ML/statistics packages such as Scikit-Learn, SciPy, Statsmodels, PyTorch, Keras.\nExperience with Power BI or Tableau.\nGood understanding of probability, statistics (hypothesis testing, design of experiments, power calculations, mixed-effect models), linear algebra, and the mathematical bases of ML methods.\nGood understanding of ML techniques for supervised/unsupervised learning, feature selection, dimensionality reduction, regression, classification, clustering, and time-series analysis.\nExperience interacting with databases and writing SQL queries.\nExperience developing, training, validating, and deploying ML/statistical models.\nExperience using data-visualization techniques for analysis and model explanation.\nExperience collaborating effectively with non-technical stakeholders; self-driven, curious, and creative.\nPreferred\nExperience with different deep learning architectures and how each applies to different problems.\nExperience with big data technologies such as Databricks, Spark, Snowpark.\nExperience with cloud technologies such as Microsoft Azure or AWS.\nExperience deploying ML solutions for production (e.g., REST APIs in Azure ML, Docker, Kubernetes) in large data-science projects.\nExperience mentoring analysts and/or data scientists.\nCausal inference, probabilistic graphical models, Bayesian statistics, and probabilistic programming languages/packages (Stan, PyMC, PyStan).\nExperience in manufacturing, IoT, vehicle data, and/or battery engineering.\nPublications in peer-reviewed journals and/or conferences in statistics, mathematics, or machine learning.\nAbout Clarios:\nClarios 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).\nTo 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.\nEqual Employment Opportunity:\nClarios, 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 .\nA Note to Job Applicants: please be aware of scams being perpetrated through the Internet and social media platforms. 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