Job Summary

Customer Analytics is a newly formed team within Chase Data & Analytics that is responsible for (1) understanding customer needs across all of Chase and (2) generating insights around those needs that are customer-centric, segment-focused, and actionable. Customer Segmentation Analytics will power the broader team and influence business decisions by (1) using data science and machine learning to build strategic customer segments, archetypes, and attributes, and (2) driving strategic segment-focused analytics on a variety of use cases like marketing, servicing, personalization, among others.

Customer Segmentation Value Analytics is looking for a Data Scientist Associate to take on a high-profile role on the team, reporting to the Head of Customer Segmentation Analytics. This individual contributor role is a unique opportunity for an experienced practitioner in data science and analytics to (1) partner with other Data & Analytics teams to understand customer data and business priorities, (2) generate insights based on impactful segmentation and analytics, and (3) influence business decisions that impact customers across all Chase lines of business. The position offers an opportunity to work with big data technologies (Hadoop, Spark, Hive, Python, Cloud technologies) and to apply analytical, data science skills and strategic skills to broad range of challenges faced by the enterprise.

Job Responsibilities

Plan, execute, and deliver customer-centric analytical projects, including: A segmentation framework, with varying levels of granularity, to deeply understand customers at scale Attributes that describe customers' financial needs and behaviors Data assets that define customer archetypes and describe them (e.g., market share, engagement, performance) Strategic analyses for a variety of segment-focused use cases (e.g., marketing, servicing, personalization) Segment-level dashboards that inform executives on critical KPIs Become a trusted partner and thought leader on customer data and data science & analytics techniques Establish and manage relationships with internal partners Deliver rapid and scalable solutions that generate high quality output Invent creative and innovative ways to answer key business questions by leveraging existing data assets or creating new ones Build expertise on customer data from all Chase lines of business Synthesize analytical findings for senior business executives in written and verbal formats and influence decisions that impact customers across Chase lines of business

Required qualifications, capabilities, and skills 

5-7 years of hands on experience in data and analytics, or related consulting in consumer financial services, with a proven record of high performance. Experience in at least one of the following required: Credit Cards, Personal Lending, Home Lending or Deposits/Investments. Experience working with P&L data, NPV and financial modeling is a great-to-have Demonstrated experience of Machine Learning and AI applications (K-means, XGB), with a record of delivering analytics that drive business value. Familiarity working in cloud based environments and developing production level code to execute applications. Tableau knowledge a plus. Exceptional problem solving and analysis skills, combined with the ability to synthesize and effectively communicate findings inside the D&A team and other stakeholders Experience working independently as well as in teams in agile environments Proven analytics skills – should have strong hands-on experience(at least 5+ years of relevant exp.) in analyzing data, extracting insights, preparing stories, recommending business the suitable solutions/strategy. Demonstrated experience in Analytics and ML applications (K-means, XGB, CART, Logistic Regression). Experience in clustering / segmentation projects is a plus. Good problem solving skills, combined with the ability to synthesize and effectively communicate findings inside the D&A team and other stakeholders

Preferred qualifications, capabilities, and skills 

SQL (at least 4 years of hands-on exp.) Python (at least 2 years of hands-on exp.) Excel (intermediate to advanced knowledge) Data Wrangling (at least 3 years of hands-on exp.) Data Visualization (beginner to intermediate)