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Job Location: Bangalore/Bengaluru
The team is responsible for (1) understanding customer needs across (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 Analytics is looking for a Data Scientist / Quant Analytics 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 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.
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 lines of business
• Synthesize analytical findings for senior business executives in written and verbal formats
and influence decisions that impact customers across lines of business
Core Skillset
• Proven analytics skills – should have strong hands-on experience(at least 3+ 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
Technical Skills Required –
• 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)
Qualifications
• 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.
• 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
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