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Data Scientist at FCA Fiat Chrysler Automobiles (Detroit, MI)

Requires a minimum of 3 years of professional experience (not counting internships, co-ops or university research)

Fiat Chrysler Automobiles is looking to fill the full-time position of 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 & Data Services (BA) department and reports through the CIO. 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 with be in areas of data exploration and preparation, data collection and integration, machine learning (ML) and statistical modelling and data pipe-lining and deployment.

The newly hired data scientist will be a key interface between the ICT Sales & Marketing team, the Business and the BA team. Candidates need to be very much self-driven, curious and creative.

Primary Responsibilities:

    • 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.
    • 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)
    • 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 champion/challenger test (A/B tests) on production systems
      • Continuously monitor execution and health of production ML models
      • Establish best practices around ML production infrastructure
    • Other Responsibilities:
      • 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: 

    • A bachelors  in computer science, data science, operations research, statistics, applied mathematics, or a related quantitative field [or equivalent work experience such as, economics, engineering and physics] is required. Alternate experience and education in equivalent areas such as economics, engineering or physics, is acceptable. Experience in more than one area is strongly preferred. 
    • Candidates should have three to six years of relevant project experience in successfully launching, planning, executing] data science projects. Preferably in the domains of automotive or customer behavior prediction. 
    • 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. 
    • All candidates must be self-driven, curious and creative. 
    • They must demonstrate the ability to work in diverse, cross-functional teams. 
    • Should be confident, energetic self-starters, with strong moderation and communication skills.

Preferred Qualifications: 

    • A master's degree or PhD in statistics, ML, computer science or the natural sciences, especially physics or any engineering disciplines or equivalent.
    • 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.
    • A specialization in text analytics, image recognition, graph analysis or other specialized ML techniques such as deep learning, etc., is preferred.
    • Ideally, the candidates are 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.