Data Scientist Manager - San Francisco

Job Description - Data Scientist Manager - San Francisco (053310) Data Scientist Manager - San Francisco

The AI & Analytics practice group at Capgemini is expanding its footprint rapidly. As part of the fastest growing digital practice within Capgemini, we work with the latest advanced analytics, machine learning, and big data technologies to extract meaning and value from data in a number of different industries, including Media & Entertainment, High Tech, Automotive, Consumer Products & Retail, Industrial Products, Manufacturing, Telecom, Aerospace & Defense, and Energy & Utilities.

The AI & Analytics group is the fastest growing digital practice at Capgemini demanding agile innovation. As part of the AI & Analytics group, you will work in a collaborative environment with internal and client resources to understand key business goals, build solutions, and present findings to client executives while solving real-world problems. If you are passionate about solving problems in the realm of cognitive computing, big data, and machine learning while utilizing business acumen, statistical understanding, and technical know‑how, the AI & Analytics practice group at Capgemini is the best place to grow your career.

• Work in a collaborative environment with global teams to drive client engagements in a broad range of industries to design and build scalable AI and Machine Learning solutions to solve business problems and create value by leveraging client data.
• Work with team to quickly understand client needs, develop solutions, and collaboratively present findings to client executives.
• Collaborate with team to provide data‑driven recommendations and solutions to clients by clearly articulating complex technical concepts through the generation and delivery of presentations.
• Analyze and model both structured and unstructured data to generate value using R and Python.
• Design and develop relational databases for collecting and storing data, and build data input and collection mechanisms.
• Responsible for data related activities such as data extraction, profiling, cleansing, de‑duplication, standardization, conversion, transformation, and loading.
• Deliver solutions in the cloud space (AWS, Azure); solution architect experience required.
• Deliver and support solutions across delivery landscape including Agile applications development, support, and deployment.

• 6+ years professional work experience as a data scientist or on advanced analytics/statistics projects with 5+ years experience in sectors such as Aerospace & Defense, Automotive, Banking, Consumer Products & Retail, Financial Services, Healthcare, High Tech, Industrial Products, Insurance, Life Sciences, Manufacturing, Public Sector, Telecom, Media & Entertainment, and Energy & Utilities.
• Master’s degree or PhD in Computer Science, Statistics, Economics, Mathematics, or other closely related field with experience in R and Python.
• Graph Theory work, possibly NEO4J.
• Experience with Deep Learning methods in NLP & Computer Vision, sentiment analysis, topic modeling, graph theory and databases.
• Experience with common data science tools such as Python, R, PyTorch, Tensor Flow, Keras, NLTK, spaCy, or Neo4j, and a good understanding of modelling platforms (Azure AutoML, Sage Maker, Databricks, Data Robot, and H2O.ai).
• Delivery/Solution should be in the cloud space (AWS, Azure). Solution Architect experience.
• Candidates should be flexible/willing to work across the delivery landscape which includes, but is not limited to, Agile Applications Development, Support and Deployment.
• Applicants for employment in the US must have valid work authorization that does not now and/or will not in the future require sponsorship of a visa for employment authorization in the US by Capgemini.
• 2-5 years (minimum 1 year relevant) experience and Bachelor’s Degree (for Data Modeler, Data Miner roles).
• Proficiency in software engineering techniques, architecture, lifecycle and data management.
• Progressing skills in business analysis, business knowledge, software engineering leadership, architecture knowledge, and technical solution design.