Lead Data Analyst at Great Eastern Life Assurance Co Ltd

Cuenca, Azuay, Ecuador -

Full Time

Start Date

Immediate

Expiry Date

17 Aug, 26

Salary

0.0

Posted On

19 May, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Analysis, Customer Lifetime Management, Customer Segmentation, SQL, Python, Tableau, PowerBI, Qlikview, Statistical Methods, Data Visualization, Big Data Ecosystems, Requirements Gathering, Problem Structuring, Stakeholder Influence, Business Intelligence, Data Triangulation

Industry

Insurance

Description

The Lead Data Analyst is responsible for delivering deep analytical insights to support customer lifetime management and uncover hidden drivers of customer behaviour. At its core, this role focuses on understanding customer behaviour across the entire lifecycle using data-driven approach – prospecting, acquisition, engagement, cross-sell & up-sell and retention to create long term customer value. The role entails the use of advanced analytical approach and hypothesis driven analysis. The Lead Data Analyst is expected to investigate complex business questions by breaking down the problem and triangulating across multiple datasets including structured and unstructured data. These analyses would require strong analytical and business judgment and analytical rigor. Insights generated must be evidence-based and actionable. Success is measured by the ability to influence tangible improvements in customer metrics. Analyse and identify key drivers across the customer lifecycle stage Perform customer segmentation for targeted customer strategies Conduct structured deep dive analyses and interpret trends of customer behaviour Isolate root causes using data triangulation across multiple data sources and applying statistical methods to enrich customer insights Design, build and enhance visual analytics assets to ensure continued relevance and usage of the dashboards by business users for making informed decisions Provide mentorship and guidance to other data analysts on analytical approaches and problem structuring Ensure all analytics project undertaking comply with both internal and external policies, guidelines and regulations Takes accountability in considering business and regulatory compliance risks and takes appropriate steps to mitigate the risks. Maintains awareness of industry trends on regulatory compliance, emerging threats and technologies in order to understand the risk and better safeguard the company. Highlights any potential concerns /risks and proactively shares best risk management practices Master or Bachelor of Science degree in Business Analytics, Statistics, Economics, Actuarial Science, Data Science, Computer Science, Mathematics or equivalent disciplines that focuses on extensive use of data for analysis A minimum 7 years of experience participating in business intelligence and analytics projects as an individual contributor required A minimum 7 years of experience in data visualization tools (Tableau / PowerBI / Qlikview) is required A minimum 7 years of experience in SQL and Python programming is required Ability to analyze, identify, visualize and describe key trends within large datasets Hands-on experience with large volumes of data using SQL, Spark, Hadoop, or other big data ecosystems is preferred Good at requirements gathering, multitasking and work prioritization and being able to deliver work timely under pressure Understanding of banking, insurance and financial services is preferred Curiosity & a real passion for understanding “why?” Creativity to see possibilities within the data & translate into compelling stories, decision and actions for non-technical business users Strong communication skills and ability to influence the business to take action using data High level of integrity, takes accountability of work and good attitude over teamwork. Takes initiative to improve current state of things and adaptable to embrace new changes

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Responsibilities

The Lead Data Analyst delivers deep analytical insights to understand customer behavior across the entire lifecycle to create long-term value. This includes performing customer segmentation, building visual analytics assets, and providing mentorship to other analysts.