Vice President, Data Science Practice Lead

Key Skills

Java Programming, Design, Python Programming, SQL, Data science techniques, Deep Learning, Keras software library, Natural Language Processing (NLP), TensorFlow, MxNet, Computer Vision (CV), Knowledge graph, Information Retrieval, H2O, Causal inference

Job Description

Fidelity Personal Investing is seeking a Data Science Practice Lead with a strong publication record and extensive experience developing and deploying ML enabled products and services at scale, with significant expertise in deep learning preferably in the context of document and text processing, computer vision and joint vision and text models. As this is a leadership role, it requires candidates with expertise leading data science teams and engaging business partners on data science opportunities.

The Team

Fidelity Personal Investing is seeking a top level data science leader to join the Personal Investing AI and Data Science Center of Excellence (CoE). Our CoE is a team of data scientists in the US and India employing a wide array of AI and data science techniques such as NLP, deep learning, machine learning, causal inference, predictive analytics, experimental design, and optimization to solve a wide variety of business problems ranging from business-to-consumer (B2C), business-to-business (B2B), to quality control, operations management, and automation.

This hard-working professional will direct a team of data scientists, collaborate closely with data engineers, data experts, quality engineers, and business partners to solve complicated text analytics problems with innovative solutions. Candidate is encouraged to have significant leadership experience in a corporate, consulting, or academic environment, as well as strong academic qualifications with a consistent record of research publications in relevant fields. Successful Candidate must be comfortable in a fast-paced, unexpected and sometimes ambiguous environment working with current and emerging AI technologies. In addition to owning delivery of AI-enabled applications, the assignments will include providing analytic consulting to the business, identifying and capturing complex data from multiple sources, conducting experiments to test algorithms, Supervising model performance, interpretation of findings, business presentation to senior management, and guiding and training technical and non-technical audience.

The Expertise and Skills You Bring

• PhD in Computer Science (or equivalent) with at least 7 years of relevant professional experience. Candidates with MSc degree and substantial experience are encouraged to apply.
• An established track record performing applied research in one or more of he following areas: machine learning, computer vision, recommender systems, natural language processing, time series modeling, transformers, knowledge graphs and their applications in search, personalization and marketing.
• Demonstrated track record leading and handling data science teams and delivery of industry leading AI-enabled products and services.
• List of all publications and presentations in your resume is encouraged, and inclusion of electronic copies of selected publications is welcome
• Knowledge of most of the following quantitative fields: Computer Vision, Natural Language Processing, Reinforcement Learning, Recommendation System, Time Series Models, Knowledge Graph, Causal Inference, Information Retrieval and experimental design
• Statistical and data science programming skills using Python
• Experience in projects involving large scale multi-dimensional databases, sophisticated business infrastructure, and cross-functional teams. Three to five optimally launched ML projects would be ideal.
• Knowledge of Deep Learning and related tools such as TensorFlow, Keras, MXNET, or H2O is preferred

The Value You Deliver

• Set a strategic direction for and provide innovative solutions to customer- and associate-centric projects (text mining, document/image processing, document-processing workflows, etc).
• Guide data science and data engineering teams on delivery of projects and capabilities at scale.
• Engage business partners to complete their priorities and identify new opportunities to translate their business objectives to problems to solve and capabilities to deliver.
• Implement new technologies in a production environment with product, IT, and data engineering teams
• Present reports and findings to senior level technical and non-technical audiences