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Synchrony

AVP, Data Science (L11) @ Synchrony

location icon Hyderabad IN
Full Time

Job Description:

Role Title: AVP, Data Science (L11)

Company Overview:

COMPANY OVERVIEW: Synchrony (NYSE: SYF) is a premier consumer financial services company delivering one of the industry’s most complete digitally enabled product suites. Our experience, expertise and scale encompass a broad spectrum of industries including digital, health and wellness, retail, telecommunications, home, auto, outdoors, pet and more.

  • We have recently been ranked #5 among India’s Best Companies to Work for 2023, #21 under LinkedIn Top Companies in India list, and received Top 25 BFSI recognition from Great Place To Work India. We have been ranked Top 5 among India’s Best Workplaces in Diversity, Equity, and Inclusion, and Top 10 among India’s Best Workplaces for Women in 2022.

  • We offer 100% Work from Home flexibility for all our Functional employees and provide some of the best-in-class Employee Benefits and Programs catering to work-life balance and overall well-being. In addition to this, we also have Regional Engagement Hubs across India and a co-working space in Bangalore.

Organizational Overview:

Our Analytics organization comprises of data analysts who focus on enabling strategies to enhance customer and partner experience and optimize business performance through data management and development of full stack descriptive to prescriptive analytics solutions using cutting edge technologies thereby enabling business growth.

Role Summary/Purpose :

The AVP, Data Science is responsible for being a Model development lead(“MDL”) on partner and vendor-developed models include leveraging and developing statistical models to identify fraud and account linkage along with maintaining production fraud research rules and reporting in a neo4j/linkurious database and Cypher coding. This role requires a high level of expertise with minimal technical supervision to serve as “MDL” on a wide range of model categories. In addition, this role will be the liaison of the model stakeholders team and model risk team for partner and vendor-developed models across Synchrony’s Financial Crime Analytics, including Consumer/Commercial Acquisitions, Transactions, Customer Behaviors, Customer Experience, Marketing and Operational Risk. To support One synchrony culture, he/she will also be responsible for building and leveraging relationships across multiple functions within Analytics

Key Responsibilities:

  • Lead the discussions with business stakeholders, third-party vendors and partners to design appropriate model specifications to solve business problems and to develop models for Synchrony that align with Model Risk Management (“MRM”) standards and Model Development procedures and evaluate such solutions for their efficacy and risk. Write model documentation and support the model validation process. Ensure modeling process compliance with internal procedures, standards, policies, and external regulatory guidance.

  • Maintain and develop rules in neo4j in support of fraud investigations

  • With direction from the Fraud Technology and Special Investigations Team, develop graph data science tools to assist in improving current rules and strategies for the early detection of fraud and benefit of investigations. 

  • Work with Technology and Operations team to implement analytical and detective tools to facilitate customer experiences, ensure business efficiencies and provide cost benefits through technology.

  • Keep pace with the latest developments in statistical modeling, data science, machine learning, artificial intelligence, risk technology (vendor and in-house), and regulatory environment in order to provide guidance to stakeholders

  • Enabling Fraud Technology teams to use relevant models to enhance strategies

  • Support stakeholders on projects related to root cause analysis (including handling escalated discussions with stakeholders that require savvy solutions to complex problems), regulatory examinations and internal audits of the modeling process and selected model samples.

  • Perform and provide assessments of all aspects of models including theoretical aspects, model design and implementation, data integrity and reliability, sufficiency of outcomes analysis, and effectiveness of the ongoing monitoring. Continuously drive better model risk management practices and add value to the business through more efficiency, stronger controls, better processes, and strong partnerships with model stakeholders.

  • Extract, Manipulate datasets from source systems utilizing SQL, Python Spark, SAS etc. and to effectively manage projects and team with proactive communication with stakeholders and to build strong relationships with US and IAH teams to communicate the value proposition of the team to drive engagements and business impact.

Required Skills and Knowledge:

  • Minimum 6+ years of hands-on experience in building predictive models (using techniques like logistic, linear regression, decision tree, clustering, Random Forest, XG Boost, Text Mining – Topic Modeling, Sentiment Analysis etc.)

  • Minimum 6+ years of hands-on Analytics Experience with SQL, SAS, Python, Spark, Scala, and Hive Tools with the ability to leverage advanced algorithms and be efficient in handling complex/large data

  • Experienced with network science, graph analytics tools, preferably Neo4j, graph machine learning

  • Excellent verbal/written skills and stakeholder management skills are critical as this role entails communicating technical and complex insights to both technical and non-technical audiences across all levels of the organization.

  • Strategic thinker with an ability to innovate and find creative solutions to meeting business goals and Strong grasp of business aspects (like financial P&L drivers, credit card dynamics, etc.), demonstrated through seamless delivery on own and team's projects.

  • Strong skills in project management, communications, multi-tasking, ability to work independently, and relationship management. Demonstrated ability to manage complexity and multiple initiatives under aggressive timelines. Ability to synthesize/analyze diverse information, develop and recommend strategies, and articulate clearly to business partners and customers.

Desired Skills and Knowledge:

  • Knowledge of Cypher programming language and neo4j/TigerGraph/Neptune

  • Prior exposure and understanding of U.S. credit card business, fraud and financial/banking crimes

  • Exposure to big data programming - Pyspark, Scala etc.

  • Exposure to predictive tools like H2O, SAS Viya, AWS SageMaker, Alteryx etc.

  • Experience with the application of regulatory requirements for Model Risk

Eligibility Criteria:

BS or MS in Statistics, Economics, Mathematics, Engineering, or another quantitative field with 6+ years of hands-on Analytics/Data science experience, or in lieu of a degree with 8+ years of experience.

Work Timings: 2pm to 11pm IST (This role qualifies for Enhanced Flexibility and Choice offered in Synchrony India and will require the incumbent to be available between 06:00 AM Eastern Time – 11:30 AM Eastern Time (timings are anchored to US Eastern hours and will adjust twice a year locally). This window is for meetings with India and US teams. The remaining hours will be flexible for the employee to choose. Exceptions may apply periodically due to business needs. Please discuss this with the hiring manager for more details)

For Internal Applicants:

  • Understand the criteria or mandatory skills required for the role, before applying

  • Inform your manager and HRM before applying for any role on Workday

  • Ensure that your professional profile is updated (fields such as education, prior experience, other skills) and it is mandatory to upload your updated resume (Word or PDF format)

  • Must not be any corrective action plan (First Formal/Final Formal, PIP)

  • L9+ Employees who have completed 18 months in the organization and 12 months in current role and level are only eligible.

  • L09+ Employees can apply

Grade/Level: 11

Job Family Group:

Data Analytics

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