Director Data Science -- AI/ML

Full Time

Job Description

SUMMARY: The Analytics Delivery Director within the Analytics Center of Excellence (CoE) is a hands-on leader who will be responsible for delivering business value by delivering high-impact business targeted business outcomes through introduction, modernization, and adoption of best in-class advanced analytics including machine learning and AI. The Analytics Delivery Director is a thought leader with significant experience and market awareness in working with various use cases in financial services industry and complex environment supporting multiple LOBs and corporate functions. The Analytics Delivery Director will be responsible for working collaboratively with cross functional business leaders and IT stakeholders to identify opportunities to apply statistical methods to improve business performance, generate revenue, identify new market opportunities, and gain operational efficiencies. The data science leader will be responsible for interpreting and developing detailed reports, presentations to the senior management with clear and concise message (decode the black box) to articulate results of statistical analysis and tables understandable by non-statisticians. This role will entail assisting and providing directions to the data scientists and data engineers in development of a data science models using cloud-based architecture that would support strategic business initiatives. The Analytics Delivery Director will also be responsible for evaluating analytics, data engineering and wrangling tools, developing best practices, know-hows, conducting educational sessions and act as subject matter expert in AI/ML in Cloud. Work collaboratively with Data Architecture, Information Security and Cloud Service providers and other vendors to enable access to data, tools, and technologies in a secure and easy to use manner to accelerate analytical capabilities within the Bank.

ESSENTIAL DUTIES AND RESPONSIBILITIES include the following. Other duties and special projects may be assigned.
  • Work collaboratively with business leaders to facilitate discussions to identify high-impact analytics use cases to drive revenue and improve efficiencies
  • Develop, deploy and maintain predictive models including machine learning and AI.
  • Influence the decisions on platform engineering, data architecture, information security, cyber security, data engineering and data science for optimal solutions striking the right balance to make data available for governed and self-service data science citizen development
  • Provide strategic recommendations to accelerate and maximize the use of data assets by empowering the data science across the enterprise
  • Work closely with IT leaders to build data consumption platforms to support high-quality data availability for data-driven decisions through AI/ML Models
  • Assist developing analytics roadmap to deliver incremental value by taking into account various factors including business priorities, dependencies and ROI.
  • Delivery analytics model management, develop automation and maintenance programs
  • Define and Designing & implementing MLS Ops model, operationalizing data science platform, leveraging advanced data modeling, machine learning, predictive modeling and analytical techniques to interpret key findings from available data and leverage these insights to support business outcomes and increase stakeholder value
  • Perform market scan for latest analytics technology trends, competitive industry updates as it relates to analytics, educate users on the art-of-the-possible
  • Lead and champion Analytics Working Group for knowledge sharing, best practices and to ensure compliance with other IT Policies and Procedures including data governance
  • Maximize value derived from data and analytics: Foster value creation using the organization's data assets, as well as the external data ecosystem. This includes aiding value creation through data exploitation, envisioning data-enabled strategies, as well as enabling all forms of business outcomes through analytics, data and analytics governance, and enterprise information policy
  • Develop Proof-of-Concepts and Pilots in a short timeframe either hands on or working in a very small team
  • Secure data and analytic assets: Aid in the analysis of data and analytics security requirements and solutions, and work with the chief information security officer (CISO) and CDO to ensure that enterprise data and analytics assets are treated as a protected asset
  • Be in the forefront of all analytics projects to provide optimal technology directions
  • Lead and mentor data scientists and data engineers
  • Foster development best practices within the team
  • Identify and drive process improvements
  • Facilitate communication with cross-functional groups


  • Education: Bachelor's or Masters degree in Mathematics, Statistics, Computer Science, Machine Learning, AI or in another related field (At least 10 years), required And
  • 10+ years of experience and at least 5 years of hands on experience in building analytical models working in data science related field which includes hands on experience in developing machine learning models required
  • 5+ years of experience managing data analytics professionals including machine learning scientists, data scientists, research scientists, applied scientists, data engineers and/or economists required
  • Ability to distill informal customer requirements into problem definitions, dealing with ambiguity and competing objectives required
  • Ability to manage and quantify improvement in customer experience or value for the business resulting from analytics model outcomes required
  • ience with programming languages Dataiku, DataRobot, RapidMiner, SageMaker, R, MATLAB, Python or similar preferred
  • 10+ years' experience in Data and Analytics and, with at least five years hands on experience in delivering machine learning and AI models for business growth
  • Hands on experience in using Python, R, Sagemaker or any other commercially off-the shelf products like Dataiku, SAS, Alteryx
  • Experience leading analytical teams and a track record of developing teams to produce successful Data Science solutions that deliver significant benefit to business preferred
  • Experience in using Data Lakes, Data Warehouses and 3rd party data
  • In-depth experience of designing and implementing analytics for large organizations
  • Hands-on experience with implementing data and analytics management programs is preferred
  • System integration experience, including interface design, and familiarity with web-oriented architecture techniques
  • Data modeling and information classification expertise at the enterprise level
  • Understanding of common information architecture frameworks and information models
  • Understanding of metamodels, taxonomies and ontologies, as well as of the challenges of applying structured techniques (data modeling) to less-structured sources
  • Familiarity with MDM, business intelligence, and data warehouse design and implementation techniques
  • Experience with distributed management and analytics in cloud environments
  • Expert level understanding of a variety of data access and analytics approaches
  • Ability to assess rapidly changing technologies and apply them to business needs
  • Experience with AWS data warehouse and database services (Redshift, RDS, Aurora, etc EMR, Glue, etc)
  • Experience with data visualization tools such as Tableau, Qlik, PowerBI, etc
  • Experience in implementing security at role and row level, object level, obfuscation, encryption to secure data
  • Experience in team building, hiring, mentoring data scientists and data engineers
  • Familiar with Sarbanes-Oxley, Payment Card Industry Data Security Standard (PCI-DSS), General Data Protection Regulation (GDPR), Privacy Practices, CSA Framework
  • Experience in model validation and presentation of models in simple and easy to understand business language
  • Strategic planning skills -- must interpret business and technology requirements, identify dependencies and develop practical roadmaps to deal with these drivers
  • Communication skills -- will be required to translate complex data-related matters into business terms that are readily understood by colleagues The data architect should anticipate presenting analyses in person and in written formats
  • Ability to stay composed in the face of opposition to architectural principles, governance and standards
  • Self-motivated and work independently

Dice Id : RTL193219
Position Id : 4973
Originally Posted : 2 months ago
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