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Engaged Employer

Expedia Group

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3.8

based on 289 Reviews

68 Expedia Group Jobs

Principal Data Scientist, Analytics

Expedia, Inc

3.8

based on 289 Reviews

7-11 years

Gurgaon / Gurugram

1 vacancy

Principal Data Scientist, Analytics

Expedia Group

posted 4d ago

Job Role Insights

Flexible timing

Key skills for the job

Data Science Consulting SQL Machine Learning Talent Acquisition Analytical Chemistry

+ 4 more

Job Description

Why Join Us?

To shape the future of travel, people must come first. Guided by our Values and Leadership Agreements, we foster an open culture where everyone belongs, differences are celebrated and know that when one of us wins, we all win.

We provide a full benefits package, including exciting travel perks, generous time-off, parental leave, a global hybrid work setup (with some pretty cool offices), and career development resources, all to fuel our employees passion for travel and ensure a rewarding career journey. We re building a more open world. Join us.

Role Summary

An indispensable and highly influential individual contributor who is expert in an analytical capability. Expertly translates vague business requirements into analytics projects ready for delegation to other analysts. Assists with strategic projects and change initiatives of the wider group where needed. Coaches senior individual contributors in their skills and regularly interfaces with executive (VP/SVP+) leadership.

Principal, Data Science - Analytics

At Expedia Group, our mission is to power global travel for everyone, everywhere. If you are passionate about building incredible customer experiences and seek a collaborative, high-growth, and fast-paced environment, you will love Expedia Group s InsurTech team! In InsurTech, we provide confidence to book, peace of mind, and protection when the unexpected happens for our travelers globally through an innovative portfolio of customer centric, risk-based products.

Our team is looking for an outstanding leader with a talent for analytics, a strategic approach, and an orientation for action to join the InsurTech team as Principal Data Science, Analytics. In this role, you will be driving our pursuit of product innovation, business monitoring, and strategic decisions in close partnership with team members across strategy, product, supply, technology, and data science.

This is an exciting opportunity to join an upbeat and international work environment, where data is at the core of everything we do and customer centricity is top of the agenda!

In this role, you will:

Define a strategy for delivery of a highly leveraged set of BI products on the topics of InsurTech product performance, operational efficiency and risk management
Drive expansion of our data platform while adopting EG data strategy/best practices to accelerate data access and data coverage
Develop a deep understanding of our customers behavior throughout their journeys enabling us to make better product decisions around Customer Lifetime Value and Repeat.
Be a thought-leading partner for our Analytics team to deliver meaningful business insights, analytics consulting and education to our global InsurTech team and stakeholders
Define, implement and standardize metrics, reports and self-service dashboards to enable data driven decisions across the organization.
Partner across a wide set of domains and functional teams to accelerate delivery of deep business insights and concrete recommendations based on data and analytics.
Coaches other levels on the team and provides valuable support to peers as a member of analytics LT. Provides leadership to the wider team in enhancing our data strategy, up-leveling our tool kits and establishing best practices.
Leads large, strategic, complex analytics projects - often crossing collective boundaries. Comfortable interfacing with stakeholders of all levels including Senior Directors/VPs and presenting to larger audiences (i.e. product/marketing orgs, all hands etc.)

Experience and qualifications:

Must Have:

Educational Background : Bachelor s degree (or equivalent) in Statistics, Mathematics, Economics, Data Science, or a related field.
Experience : 10+ years of analytics experience with a focus on data extraction, processing, and analysis.
Technical Expertise : Strong proficiency in big data tools (SQL, Python, Databricks, AWS, etc.) for working with large, complex datasets.
Advanced Analytical Skills : Expertise in experimentation, causal inference, and advanced analytics, including the ability to dive deeper into A/B test data beyond statistical significance.
Cross-functional Collaboration : Proven track record of collaborating with teams across Product, Finance, and other departments to integrate data insights into business strategies.
Strategic Communication : Exceptional ability to present data insights and drive strategic discussions with senior leadership.
Influential Individual Contributor : A self-driven expert in analytics who thrives in influencing decisions and shaping strategy.
Problem-Solving & General Management : Strong analytical, problem-solving, and leadership skills to manage complex initiatives and cross-functional relationships.
Data Science & Machine Learning : Practical knowledge of advanced analytics methods, including machine learning principles and techniques.
Project & Stakeholder Management : Strong project management skills with the ability to work across multiple teams and prioritize competing demands.
Driving Change & Innovation : A willingness to challenge the status quo and lead initiatives that promote operational excellence, creativity, and innovation.

Good to Have:

Industry Experience : Background in Insurance, Banking, or FinTech is a plus.
Economic Modeling : Knowledge of economic modeling techniques such as price elasticity and time series analysis is beneficial.
Multivariate Analysis : Familiarity with regression, classification, and other multivariate analysis methods.
Product Metrics Expertise : Experience in measuring and forecasting key product metrics such as Lifetime Value (LTV), retention rates, etc.
Machine Learning Models : Exposure to machine learning models such as recommendations, multi-armed bandit algorithms, or reinforcement learning.