Fiat Chrysler Automobiles is looking to hire a Data Scientist. This position is responsible for delivering insights to the commercial functions in which FCA operates. The Data Scientist is a role in the Business Analytics and Data Services (BA) department. They will play a pivotal role in the planning, execution, and delivery of data science and machine learning-based projects. 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. The newly hired Data Scientist will be a key interface between the ICT sales and marketing team, the business and the business analytics team. Candidates need to be self-driven, curious and creative.
Primary responsibilities include:
Problem Analysis and Project Management: 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 data
Data 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
Additional responsibilities include but are not limited to:
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
Bachelor's degree in Computer Science, Data Science, operations research, Statistics, Applied Mathematics, or a related quantitative field
Minimum three years of relevant project experience in successfully launching, planning, 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
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
Minimum six years of experience launching, planning, and executing data science projects
Experience in domains of automotive or customer behavior prediction
Experience in one or more of the following commercial/open-source data discovery/analysis platforms: RStudio, Spark, KNIME, RapidMiner, Alteryx, Dataiku, H2O, SAS Enterprise Miner (SAS EM) and/or SAS Visual Data Mining and Machine Learning, Microsoft AzureML, IBM Watson Studio or SPSS Modeler, Amazon SageMaker, Google Cloud ML, SAP predictive analytics
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
Our benefits reflects the FCA commitment to helping you reach your personal and professional goals. In addition to an environment that promotes career development, we offer benefits for a healthy lifestyle and a rewarding future, designed to take care of you and your family, in various stages of life.
As a global company, our employee packages will vary by country, customary norms and the legal entity into which you are hired.