Ml & data science engineering manager
in this role, this is a great opportunity to drive the delivery of a key enterprise objective in building ford's flagship products - bring innovation in manufacturing to have significant business impact. You will spearhead ford's transformation of manufacturing by building the intelligent backbone that will power our factories of the future. As the engineering manager for data science, you will establish and lead a new, specialized team in mexico dedicated to creating ai-native infrastructure and processes within our manufacturing it ecosystem. Your mission is to harness the power of predictive modeling, machine learning, and real‑time data to solve complex manufacturing challenges, drive efficiency, and ensure the quality of our iconic vehicles. This is a rare opportunity to lead a team that will put its signature on the future of how ford manufactures vehicles.
About ford manufacturing + technology
ford is transforming how vehicles are built—from traditional assembly lines to digitally connected, intelligent manufacturing systems. At the core of this transformation is software: enabling smarter decisions, predictive maintenance, real‑time process optimization, and seamless data flow between machines, people, and the cloud.
Required qualifications
a bachelor's degree in computer science, statistics, engineering, or a related quantitative field.
3+ years of formal leadership experience, managing and mentoring technical teams in a data science or software engineering capacity.
5+ years of hands‑on experience in data science and machine learning, with deep expertise in predictive modeling, classification, and unsupervised learning techniques (like anomaly detection).
Proven experience building and deploying machine learning models into production environments.
Strong proficiency in python and common data science/ml libraries (e.g., scikit‑learn, tensorflow, pytorch, pandas).
Experience with cloud computing platforms (e.g., gcp, azure, or aws) and their associated ai/ml services.
Excellent problem‑solving skills and the ability to navigate complex, ambiguous challenges.
Fluency in both english and spanish, with strong verbal and written communication skills.
Preferred (even better if)
a master's degree or phd in a relevant technical field.
Experience in a manufacturing, industrial automation, or iot environment.
Deep knowledge of mlops principles and experience with related tools (e.g., kubeflow, mlflow, seldon core).
Experience with real‑time data streaming technologies (e.g., kafka, flink) and time‑series analysis.
Familiarity with containerization and orchestration technologies (e.g., docker, kubernetes).
A strong portfolio of deployed ai/ml projects that have delivered measurable business value.
Equal opportunity statement
ford motor company is an equal opportunity employer, as we are committed to a diverse workforce, and do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity and/or expression, status as a veteran and basis of disability.
Key responsibilities
lead and mentor: recruit, hire, and develop a high‑performing team of ai/ml and data science engineers. Foster a culture of technical excellence, innovation, and continuous learning.
Define technical strategy: architect and drive the roadmap for building ai-native infrastructure, platforms, and processes tailored for the manufacturing environment.
Oversee model development & deployment: guide the end‑to‑end lifecycle of machine learning projects—from data acquisition and feature engineering to model training, validation, and deployment of predictive models and anomaly detection systems in production.
Champion mlops: implement and evangelize best practices for mlops to ensure the scalability, reliability, and continuous improvement of our machine learning systems.
Drive business impact: collaborate closely with manufacturing operations leaders, plant floor engineers, and it partners to identify high‑value use cases and translate business needs into tangible ai‑driven solutions.
Ensure technical excellence: set and maintain high standards for code quality, system performance, and scientific rigor across all data science and machine learning projects.
Manage execution: lead the team using agile methodologies to deliver projects on time, ensuring clear communication with all stakeholders and managing priorities effectively in a dynamic environment.
Head of data science – data science manager – director data science – associate data science (pharma)
position: data science and ai manager / sr. Managerduration: long term contract
key responsibilities
product / brand & therapeutic area insights: develop and deploy advanced analytical models, predictive algorithms, and ai‑powered tools to generate actionable insights for us commercial strategies. Own end‑to‑end delivery of data science solutions—from problem framing and solution design to insight generation and recommendations.
Analytics pod collaboration: collaborate within cross‑functional analytics pods, including insight strategy & execution and market research teams. Co‑develop and execute comprehensive brand analytics strategies aligned with business objectives. Deliver integrated insights and actionable recommendations to us commercial teams. Represent data science in pod discussions and strategic planning. Ensure seamless integration of insights into decision‑making and strategic initiatives.
Innovation data science capability development: design, develop, and scale innovative ai/ml solutions from pilot to enterprise deployment. Apply agile methodologies to accelerate delivery while maintaining quality and innovation. Continuously refine and enhance solutions based on feedback and evolving business needs. Drive adoption of new capabilities and support broader organizational transformation.
Cross‑functional collaboration: partner with analytics engineering to ensure scalable and robust data ecosystems for modeling. Collaborate with digital teams to leverage digital data sources and enhance analytics capabilities. Work across teams to integrate data science solutions into enterprise workflows and systems.
Qualifications
bachelor’s degree with 7+ years of experience in engineering, economics, statistics, computer science, or a related quantitative field. Or advanced degree with 3+ years of experience in applied econometrics, statistics, data science, machine learning, mathematics, operations research, or related field.
Proven experience applying data science models to solve business problems in real‑world settings.
Experience working with sql/nosql databases and large‑scale data environments (e.g., hadoop, snowflake, databricks).
Proficiency in programming languages and ml tools such as python, r, java, or scala.
Experience with data visualization tools such as tableau, dash, or angular‑based solutions.
Experience supporting commercial strategies and tactics, preferably in the pharmaceutical or healthcare industry.
Strong stakeholder management and communication skills, with the ability to simplify complex concepts.
Ability to clearly articulate assumptions, limitations, and caveats in data and models in a business context.
What you’ll do
lead and define the organization’s data science strategy.
Build and grow high‑performing, impactful teams.
Champion data‑driven decision‑making across all business areas.
Oversee budgets and maximize roi on data initiatives.
Set the standard for data governance, ethics, and privacy.
What we’re looking for
deep expertise in machine learning, statistical modeling, and advanced analytics.
Experience with nlp, deep learning, and modern data science ecosystems.
Strong business mindset with a focus on outcomes.
Proven leadership managing and scaling technical teams.
Hands‑on experience with tools like python, tensorflow, pytorch, and cloud platforms.
Excellent communication skills to influence senior stakeholders.
Solid understanding of data governance and responsible data practices.
Experience that stands out
8+ years in data science or machine learning roles.
3+ years leading enterprise‑level data science programs.
Experience managing large initiatives, budgets, and business impact.
Education
degree in computer science, data science, statistics, mathematics, or related field.
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