ANALYTICS SCIENTIST JOB DESCRIPTION

An Analytics Scientist turns raw behavioral data into the forecasts and segmentation models that product and marketing teams use to make spending decisions. Numbers alone do not explain why a retention metric dropped or which customer cohort is about to churn. The role spans quantitative modeling, stakeholder translation, and strategic prioritization within a cross-functional product organization. Owning the analytical layer between raw event data and executive planning cycles, the Analytics Scientist sets the evidentiary standard for how product and growth decisions get made.

As the Analytics Scientist, you lead the design and execution of quantitative models, cohort analyses, and forecasting frameworks that directly shape customer engagement and retention strategy across digital product lines. You sit within a cross-functional team spanning Product, Finance, Marketing, and Data Science, translating data signals into plans that quarterly and annual roadmaps depend on.

Career Impact: Sustained work in customer segmentation and lifetime value modeling at product scale builds a depth of applied quantitative expertise that is recognized across growth-stage and enterprise technology employers.

Business Impact: The forecasts and behavioral insights this role produces inform how product and marketing budgets are allocated across millions of customer touchpoints each quarter.

Growth Opportunity: Ownership of KPI strategy and A/B testing methodology across product lines positions the Analytics Scientist to move into analytics leadership or principal data science roles within two to three years.

• Define and prioritize the analytics agenda for customer engagement and retention, translating business objectives into measurable research questions.
• Design statistical models and segmentation frameworks to identify behavioral drivers across customer cohorts and product surfaces.
• Monitor weekly performance metrics, diagnose root causes of significant changes, and deliver forecast guidance to planning stakeholders.
• Partner with Finance, Product, and Marketing teams to build data science models that support quarterly and annual planning cycles.
• Develop automated dashboards and self-service reporting tools that give cross-functional teams direct access to decision-ready data.
• Conduct deep-dive analyses across conversion rates, signup funnels, and engagement metrics to surface revenue optimization opportunities.
• Validate data quality and consistency across new and existing sources to ensure analytical outputs meet organizational standards.
• Present findings and model-driven recommendations to stakeholders ranging from individual contributors to senior leadership.

• Bachelor's degree in Statistics, Mathematics, Data Science, Economics, or a related quantitative field, or equivalent work experience.
• 3 or more years of quantitative analysis experience, with a demonstrated focus on customer behavior, product analytics, or growth strategy in a digital or e-commerce environment.
• Proficiency in statistical modeling, machine learning concepts, and data mining methods applied to real business problems.
• Strong ability to write complex queries against large datasets using SQL or equivalent query languages.
• Experience with scripting languages such as Python or R for data manipulation, modeling, and analysis automation.
• Demonstrated skill in translating analytical outputs into clear, business-contextualized narratives for non-technical audiences.
• Experience conducting A/B tests and interpreting experimentation results to support product or marketing decisions.

• Master's degree in a quantitative discipline such as Statistics, Data Science, Business Analytics, or Computer Science.
• Experience with time series analysis, cohort modeling, and lifetime value estimation in a subscription or consumer internet context.
• Background in Financial Planning and Analysis support, including building models that feed into quarterly business reviews or annual operating plans.
• Familiarity with data architecture concepts including ETL pipelines and data warehousing in a cloud environment.

• Forecast accuracy rate, measured against actual engagement and retention outcomes over rolling 90-day periods.
• Cohort retention delta, tracking improvement in retention rates attributable to model-driven interventions.
• Dashboard adoption rate, reflecting the percentage of target stakeholders actively using self-service reporting tools.
• A/B test throughput, counting the number of completed and decision-ready experiments delivered per quarter.
• Time to insight, measuring average days from analysis request to stakeholder-ready output for ad-hoc requests.
• Typical tools: Query and scripting environments (commonly SQL-based warehouses and Python), BI and visualization platforms (commonly Tableau or Power BI).

• Base Salary Range: $110,000 to $155,000 annually, depending on experience and location.
• Bonus: Annual performance bonus of 8 to 15 percent of base salary.
• Equity: RSU grants common at growth-stage and public technology companies.
• Health Benefits: Medical, dental, and vision coverage for employee and dependents.
• PTO: 15 to 20 days annually, plus observed federal holidays.
• Common Perks: Remote or hybrid flexibility, learning and development budget, and home office stipend.

Figures are estimates based on general US market benchmarks and may be outdated. Adjust based on location, company size, and seniority level.

Employment decisions are made without regard to race, color, religion, sex, national origin, age, disability, genetic information, veteran status, sexual orientation, gender identity, or any other characteristic protected under applicable federal, state, or local law. Candidates requiring a reasonable accommodation during the application or interview process may request one at any time. Final offers are contingent on successful completion of a background check. Applicants must be authorized to work in the United States.

The Analytics Scientist owns the end-to-end process of applying quantitative analysis, data modeling, and data science approaches to uncover deep insights into customer behavior across surfaces impacting engagement and retention. Working within a cross-functional ecosystem alongside Finance, Product, Marketing, and Data Science teams, this role shapes strategy and informs business decisions that directly improve customer engagement outcomes.

Key Responsibilities

• Apply expertise in quantitative analysis, data modeling, and data science approaches to gain deep insights into customer behavior and journey across surfaces impacting engagement and retention.
• Use insights and data to inform strategies that increase customer engagement and retention.
• Monitor weekly performance, understand root causes of changes in metrics, and provide guidance on forecasts.
• Partner with Finance, Product, and Marketing teams to build quarterly and annual plans using data science modeling.
• Define and prioritize opportunities to increase customer engagement and retention.
• Partner with cross-functional teams including Engagement and Retention, Data Science, Finance, Product, Campaign, and Marketing to drive business growth.
• Capture, synthesize, and interpret disparate quantitative data within the context of business objectives, identify trends, and explore data through segments and cohorts.

Required Qualifications

• Master's degree in Analytics or similar discipline preferred, or equivalent combination of education and experience.
• 4+ years of hands-on experience in quantitative analysis, preferably in e-commerce, strategy, or management consulting.
• Experience in defining a business problem, collecting required data, analyzing results, and synthesizing a compelling argument.
• Strong proficiency in SQL-like languages including Hive and Hadoop, Python, and R for querying and manipulating large datasets.
• Statistical modeling experience using Microsoft Excel or Power Pivot.
• Experience with data visualization tools such as Tableau or Power BI.
• Understanding of statistical modeling, machine learning, and data mining concepts with a record of solving problems using these methods.
• Comfortable presenting insights and findings to a broad range of stakeholders, team members, and regional partners.

Embedded within a cross-functional analytics organization spanning lodging supply, Commercial Finance, Strategy, Revenue Optimization, and Product and Technology, the Analytics Scientist develops initiatives and strategies by bringing together insights from both the demand and supply environment. Working closely with Sales teams and regional partners, this role advances revenue performance through deep-dive analysis, KPI measurement, and data-driven recommendations that protect and grow partner revenue.

Core Functions

• Maintain and develop key reports to support business performance tracking.
• Grow partner revenue through optimization and new business strategies.
• Provide direction on business priorities based on expected opportunity size.
• Conduct deep-dive analysis at a partner or market level.
• Provide recommendations to improve revenue to partners, stakeholders, and the leadership team.
• Analyze revenue performance and trends in the region.
• Support Sales teams on business development initiatives and analysis requests from partners.
• Measure KPI performance including revenue, competitive, and supply metrics.
• Evaluate revenue risk for new strategies and develop mitigation plans.
• Analyze conversion rates and conversion drivers.

Qualifications and Experience

• Bachelor's degree in a quantitative discipline such as Business, Mathematics, Computer Science, or Physics, with a graduate degree preferred.
• Minimum 2 years of analytical work experience in online travel or another data-intensive commercial environment.
• Strong understanding of revenue drivers and the commercial environment, with a proven track record of identifying growth opportunities and implementing solutions.
• Excellent SQL skills with the ability to write SQL from scratch, including experience with Teradata, Presto, and Hive.
• Decent coding skills in Python.
• Experience with Tableau for reporting and data visualization, including database connections and job scheduling.
• Basic understanding of statistics and probability.
• Ideally, experience with Hadoop and understanding of conversion drivers in an online environment.
• Excellent communication skills with the ability to present complex analysis in an understandable way for varied audiences.
• Fluency in English required.

Reporting to the product and deep learning science leadership, the Analytics Scientist collaborates with engineering teams to design and implement agile model solutions that accurately predict customer demand for millions of products worldwide. Partnering with engineers and product managers, this role builds scalable measurement solutions and delivers actionable recommendations that improve demand forecasting accuracy at global scale.

Primary Duties

• Collaborate with product managers and deep learning science and engineering teams to design and implement agile model solutions for demand core models.
• Develop edge case agile models for ongoing demand measurements toward the goal of accurately predicting customer demand for millions of products worldwide.
• Use large datasets or experiments to make causal inferences or predictions.
• Work with engineers to automate science analysis processes and build scalable measurement solutions.
• Interpret data, write reports, and make actionable recommendations.
• Extract insights from data and clearly communicate appropriate triggers and actions to stakeholders.

Skills and Qualifications

• Master's degree or Bachelor's degree plus 2 years of experience in a quantitative discipline such as Statistics, Mathematics, Data Science, Business Analytics, Economics, Finance, Engineering, or Computer Science.
• 2+ years of experience working as a data scientist or in a similar role involving data extraction, analysis, statistical modeling, and communication.
• Experience modeling observational survey data and large individual-level datasets.
• Experience finding practical solutions for ambiguous and challenging business questions.
• Proficiency in Python, SQL, Stata, R, and Scala.
• 0-2 years of research experience in statistical analysis.
• Superior verbal and written communication skills, with the ability to convey rigorous mathematical concepts to non-experts.

Sitting at the intersection of financial planning and consumer analytics, the Analytics Scientist supports the Financial Planning and Analysis org by partnering with all departments to drive financial decisions and deliver insights including automated dashboards, ad-hoc analysis, and self-service reporting tools. Operating across technical and non-technical teams, this role builds domain expertise through data, establishes KPI frameworks, and enables better decision-making across the entire organization.

Duties

• Work with partners and mentors to distill problems, adapt tools to answer complicated questions, and identify trade-offs between speed and quality of different approaches.
• Manage communications with internal and external teams and collaborate with technical and non-technical colleagues to complete data projects.
• Create relevant insights from data analysis covering viewer, creator, ad sales, commerce, and content deals.
• Develop domain and product expertise through data, build trust among peers and partners, and ensure team access to decision-ready data.
• Partner with teams to establish success metrics, create tracking approaches, troubleshoot errors, and develop a common language for KPIs.
• Provide the team with ad-hoc analysis, automated dashboards, and self-service reporting tools to support business understanding.

Experience and Qualifications

• Bachelor's degree required.
• 3+ years of domain experience in a consumer internet business or in a high-velocity, high-growth product or department.
• Experience building customer insights from transaction data, including time series analysis, cohort analysis, experimentation, data visualization, and KPI strategy.
• Product analytics experience with signup funnels, engagement metrics, and retention analysis.
• Expert SQL skills and technical programming experience, especially in Go or Python.
• Experience developing dashboards using Tableau.
• Statistics knowledge and hypothesis testing experience.
• Ability to work with stakeholders with various degrees of technical expertise and lead projects from creation to execution.

A key member of the product analytics team, the Analytics Scientist generates, maintains, and monitors key performance metrics and driving indicators that support product business levers and operations. Collaborating across business stakeholders, data engineers, data scientists, and analysts, this role delivers data-driven insights and analytical solutions that optimize existing business processes and generate measurable revenue opportunities.

Functions

• Generate, maintain, and monitor key performance metrics and driving indicators that support product business levers and operations.
• Analyze performance trends to provide objective feedback on progress toward product business targets and benchmarks.
• Develop analytical dashboards and reports to provide data visibility and access to business stakeholders.
• Develop complex insights and solutions to product business problems using a variety of analytical approaches and methodologies.
• Generate revenue opportunities through proactive data-driven insights that optimize the performance and efficiency of existing business processes.
• Present findings and analysis to stakeholders and ensure alignment with product business objectives.
• Guide and monitor data management processes to ensure data quality and consistency, including acquiring new data sources and performing data discovery.
• Partner closely with business stakeholders to understand business operations and processes and proactively identify new analytics techniques and solutions.

Background and Experience

• Bachelor's degree in a quantitative field such as Statistics, Mathematics, Econometrics, or Data Science.
• Experience in Product Analytics and generating data-driven insights for products, including A/B testing for product features.
• Extensive experience across descriptive, diagnostic, predictive, and prescriptive analytics.
• Strong programming experience with SQL, R, or Python.
• Strong experience developing analytical dashboards or reports using BI tools such as Tableau or Power BI.
• Advanced skills using Microsoft Excel and Microsoft PowerPoint.
• Experience with CRM platforms such as Salesforce or Microsoft Dynamics and Advanced Analytics platforms such as Microsoft Azure or Databricks is a plus.
• Experience with data architecture, data integration, ETL, and data warehousing in a complex environment is a plus.
• Ability to work in a dynamic cross-functional environment with analysts, data scientists, data engineers, and business stakeholders.

Accurate sales optimization and channel performance depend on the Analytics Scientist, who applies statistical predictive models, machine learning, and natural language processing to uncover trends, build forecasts, and drive big data initiatives across product sales, marketing research, and pricing domains. Based within a field-force-facing analytics function, this role builds self-serve dashboards and communicates results through reports and presentations that directly support commercial leadership decisions.

Accountabilities

• Understand and use statistical predictive models such as regression and time series models to identify trends, make forecasts, and provide projected figures.
• Perform machine learning and natural language processing tasks including classification, collaborative filtering, association rules, sentiment analysis, and topic modeling.
• Develop new dashboards and self-serve capabilities that proactively enable the field force and position data for sales optimization and performance management.
• Drive initiatives focused on big data and advanced business analytics across domains such as product sales, marketing research, and pricing.
• Communicate results and educate others through reports and presentations.

Professional Experience

• Master's degree from an accredited college or university in Computer Science, Statistics, or Mathematics.
• Minimum 2 years of professional experience working as a data scientist.
• Strong knowledge in machine learning, data visualization, statistical modeling, data mining, and information retrieval.
• Experience with structured and unstructured data and command-line scripting, data structures, and algorithms.
• Proficiency in R and Python for data analysis.
• Experience with Microsoft Azure including Machine Learning Studio and HD Insight.
• Tableau certification at Developer or Administrator level.
• Experience with SQL Server and processing large amounts of data in a cloud environment.
• Strong interpersonal skills and excellent written and verbal communication skills.
• Fluency in English required.

As the Analytics Scientist, this role researches, designs, and implements scalable analytics to detect malicious behaviors in very large data volumes and meet emerging requirements for customers within an agile team environment. The wider NNCC core product team relies on this work to maintain a competitive analytics advantage through more efficient techniques, better technologies, and more performant underlying architecture handling millions of events per second.

Technical Responsibilities

• Research, design, and implement scalable analytics to meet emerging requirements for customers within an agile team.
• Prototype advanced techniques for detecting malicious behaviors in very large data volumes by working with customers and presenting results in an intelligible way.
• Detect and analyze performance bottlenecks in new and existing solutions.
• Tune analytic implementations to cope with solution scale, handling millions of events per second.
• Continuously strive for and champion the best solution via more efficient analytics, better technologies, or more performant underlying architecture.
• Present and demonstrate solutions to clients at varying levels of seniority, tailoring communications to effectively explain analytical techniques and advise on their application.
• Translate business requests into technical data requirements using strong consulting skills and requirement elicitation techniques.

Technical Qualifications

• Master's or advanced degree in a relevant technical discipline or equivalent experience in data science applied at massive scale.
• Experience applying data science techniques including at least one analytical scripting language such as Python or Scala.
• Understanding of deployment of data science models into production systems and firm knowledge of testing and evaluating model efficacy.
• Experience or knowledge in the domain of cyber security, including the Cyber Kill Chain and associated tactics and techniques.
• Experience with Spark and PySpark for large-scale data analysis on a Spark cluster.
• Proficiency with data formats such as Parquet and Avro, Airflow and Jupyter for job control, HDFS for persistence, HBase and Elasticsearch for entity data storage, and Kafka for messaging.

Analytics Scientist builds high-quality prediction systems integrated into back-end infrastructure supporting cloud-based services, applying data analytics techniques, statistical analysis, and machine learning including neural networks and advanced classifiers. The work directly supports the development of connected services across telematics, marketing, and manufacturing data by delivering accurate predictive analytics and clean, verified data pipelines.

Job Functions

• Apply data analytics techniques, statistical analysis, and machine learning to build high-quality prediction systems integrated into back-end infrastructure supporting cloud-based services.
• Select features, build, and optimize classifiers using machine learning techniques to enhance predictive analytics.
• Design and implement neural networks using state-of-the-art methods.
• Extend company data with third-party sources of information when needed.
• Enhance data collection procedures to include information relevant for building analytic systems.
• Process, cleanse, and verify the integrity of data used for analysis.
• Conduct ad-hoc analysis and present results in a clear and actionable manner.

Education and Experience

• MS or PhD degree in Statistics, Operations Research, Electrical Engineering, Computer Science, or equivalent related area.
• 3 to 10 years of experience in analytics.
• Excellent understanding of machine learning and neural network techniques and algorithms such as CNNs, Transformer Networks, SVM, and Decision Forests.
• Good applied statistics skills including distributions, statistical testing, regression, Bayesian methods, and Markov chains.
• Good scripting and programming skills in R, Python, Java, or equivalent.
• Experience with common data science toolkits such as Scikit-Learn, Spark ML, PyTorch, and Keras.
• Experience with data visualization tools such as Tableau and Matplotlib.
• Proficiency in SQL or equivalent query languages and strong working knowledge of Excel for data analysis and reporting.

The Analytics Scientist delivers advanced reporting and data visualization capabilities on large volumes of data, translating business requirements into high-quality solution specifications and repeatable modeling processes that support analytics needs across products, markets, and services. Working alongside business stakeholders and cross-functional teams, this role ensures data quality, security compliance, and timely execution of projects with regular updates to key stakeholders.

What You'll Do

• Collaborate with various functions and teams to architect, develop, and maintain advanced reporting and data visualization capabilities on large volumes of data.
• Translate business requirements into tangible solution specifications and high-quality, on-time deliverables.
• Create repeatable processes to support development of modeling and reporting.
• Effectively use tools to manipulate large-scale databases and synthesize data insights.
• Provide first-level insights, conclusions, and assessments and present findings via dashboards, Excel, and PowerPoint.
• Apply quality control, data validation, and cleansing processes to new and existing data sources.
• Abide by security policies and practices, ensure the confidentiality and integrity of information, report any suspected security violations, and complete all mandatory security training.

Minimum Qualifications

• Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, Mathematics, or Statistics, with MS or MBA preferred.
• Experience in data management, data mining, data analytics, data reporting, data product development, and quantitative analysis.
• Experience for leading entire projects and managing timely execution, with regular updates to key stakeholders.
• Advanced SQL coding skills required.
• Experience with Hadoop environments, Python, R, and WPS.
• Experience with data visualization tools such as Tableau and Power BI.
• Financial institution or payments industry experience is a plus.
• Experience in campaign analysis and team management is a plus.
• Excellent English, quantitative, technical, and written and oral communication skills.

Embedded within a Data and Analytics multi-disciplinary team, the Analytics Scientist develops, tests, and maintains analytical models while leading the creation of approaches, methodologies, and standards for Advanced Analytics and Data Science. Working closely with internal and external clients across Digital, Customer, Retail, Finance, and HR functions, this role shapes the Data and Analytics Strategy and roadmap and ensures sound scientific principles underpin all data products and solutions.

Day-to-Day Responsibilities

• Develop, test, and maintain analytical models using new datasets as required to underpin each use case.
• Design statistical models to extract desired insights from collected data.
• Determine the optimal modeling approach for each scenario.
• Effectively communicate business implications of derived insights from applied analytics and influence adoption of recommendations.
• Lead the creation and implementation of approaches, methodologies, and standards for Advanced Analytics and Data Science.
• Support the enhancement of the Data and Analytics Strategy and future roadmap.
• Collaborate with various functions and teams across the company to develop insights and analytics solutions for internal and external clients.

Knowledge Skills and Abilities

• Master's or PhD in a relevant technical discipline such as Mathematics, Statistics, Computer Science, Engineering, Data Science, or Physics.
• Extensive experience within the same or similar role, including deep experience in Digital, Customer, and Retail Analytics with exposure to Finance and HR processes and data.
• Expertise in customer segmentation and lifetime value model development.
• Experience with relational data structures and unstructured data sources.
• Strong data visualization skills and ability to communicate data stories effectively to varied audiences.

Reporting to business unit leadership, the Analytics Scientist identifies business challenges across the organization and transforms them into analytics use cases, building and managing machine learning models that are implemented directly into business decisions and actions. Partnering with business units and technical teams across both English and Japanese-speaking environments, this role measures and reports the business value enabled by analytics to drive the organization toward data-driven decision-making.

Scope of Work

• Work with various business units to identify business challenges and transform them into analytics use cases.
• Prepare and engineer data for machine learning.
• Build, evaluate, interpret, and manage machine learning models.
• Implement models into business decisions and actions.
• Measure business value enabled by analytics solutions.
• Consult with business units to identify challenges and translate them into analytics problems.
• Report analytics projects and progress to stakeholders.

Position Requirements

• Consulting skills to identify challenges faced by various business units and transform them into analytics problems.
• Solid programming skills to prepare and engineer data for machine learning.
• Ability to build, evaluate, interpret, and manage machine learning models and implement analytics solutions into business decisions.
• Ability to measure business impact of analytics solutions.
• Communication skills to report analytics projects to stakeholders and collaborate across technical and non-technical teams.
• Business-level or native fluency in both English and Japanese required.