Within the Business Analytics area, we leverage cutting edge technologies to allow us to deliver exceptional customer experiences. Significant challenges and opportunities await those who join us in this newly created division of FCA. We are on the threshold of the biggest revolution in personal transportation. We need great people who share our passions.
Everything you do - and learn - will be leading to the future at Stellantis is a place where people are empowered to drive change, where bold challenges are taken on and an entrepreneurial spirit is ever present. Our culture never settles on the status quo; rather, collaboration, curiosity, and unconventional thinking. Every employee, regardless of position in the company, is expected to drive change and lead people.
We employ five key leadership principles that reflect our core values and provide the foundation for the cultural transformation of our workforce: We are a meritocracy. Leadership is a function of leading change and leading people. We embrace and cherish competition. We aim to achieve best-in-class performance. We deliver what we promise.
Stellantis is looking to hire a Data Scientist. This position will play a pivotal role in the planning, execution, and delivery of data science and machine learning-based projects for a customer data platform which will enable 1:1 personalization. The bulk of the work will be in areas of data exploration and preparation, data collection and integration, machine learning (ML) and statistical modeling and data pipe-lining and deployment.
Primary responsibilities include:
Problem Analysis
Guide and inspire the organization about the business potential and strategy of artificial intelligence (AI)/data science
Identify data-driven/ML business opportunities
Collaborate across the business to understand IT and business constraints
Prioritize, scope and manage data science projects and the corresponding key performance indicators (KPIs) for success
Data exploration and preparation
Apply statistical analysis and visualization techniques to various data, such as hierarchical clustering, T-distributed Stochastic Neighbor Embedding (t-SNE), principal components analysis (PCA)
Generate and test hypotheses about the underlying mechanics of the business process
Network with domain experts to better understand the business mechanics that generated the dataData Collection and Integration
Understand new data sources and process pipelines, catalog and document their use in solving business problems
Create data pipelines and assets the enable more efficiency and repeatability of data science activities
Machine Learning and Statistical Modelling
Apply various ML and advanced analytics techniques to perform classification or prediction tasks
Integrate domain knowledge into the ML solution (for example, from an understanding of financial risk, customer journey, quality prediction, sales, marketing)
Testing of ML models, such as cross-validation, A/B testing, bias and fairness, operationalization
Collaborate with ML operations (MLOps), data engineers, and IT to evaluate and implement ML deployment options
(help to) integrate model performance management tools into the current business infrastructure
(help to) implement a champion/challenger test (A/B tests) on production systems
Continuously monitor the execution and health of production ML models
Establish best practices around ML production infrastructure
Train other business and IT staff on basic data science principles and techniques
Train peers on specialist data science topics
Promote collaboration with the data science COE within the organization
Basic Qualifications:
Bachelor's degree in Computer Science, Data Science, Operations Research, Statistics, Applied Mathematics, or a related quantitative field
5 years of relevant project experience in successfully launching, planning, and executing data science projects
Coding knowledge and experience in several languages: for example, R, Python, SQL, Java, C++, etc.
Experience of working across multiple deployment environments including cloud, on-premises and hybrid, multiple operating systems and through containerization techniques such as Docker, Kubernetes, AWS Elastic Container Service, and others
Experience with distributed data/computing and database tools: MapReduce, Hadoop, Hive, Kafka, MySQL, Postgres, DB2 or Greenplum, etc.
Must be self-driven, curious and creative
Must demonstrate the ability to work in diverse, cross-functional teams
Confident, energetic self-starter, with strong moderation and communication skills
Preferred Qualifications:
Master's degree or Ph.D. in Statistics, Machine Learning, computer science or the natural sciences, especially Physics or any engineering disciplines or equivalent field
8 years of experience launching, planning, and executing data science projects
Experience in domains of automotive or customer behavior prediction
Knowledge and experience in statistical and data mining techniques: generalized linear model (GLM)/regression, random forest, boosting, trees, text mining, hierarchical clustering, deep learning, convolutional neural network (CNN), recurrent neural network (RNN), T-distributed Stochastic Neighbor Embedding (t-SNE), graph analysis, etc.
Specialization in text analytics, image recognition, graph analysis or other specialized ML techniques such as deep learning, etc.
Adept in agile methodologies and well-versed in applying DevOps/MLOps methods to the construction of ML and data science pipelines
Knowledge of industry standard BA tools, including Cognos, QlikView, Business Objects, and other tools that could be used for enterprise solutions
Should exhibit superior presentation skills, including storytelling and other techniques to guide and inspire and explain analytics capabilities and techniques to the organization
Equal Opportunity Employer Minorities/Women/Protected Veterans/Disabled.