ABOUT THE ROLE AND OUR TEAM

TKWW is looking for a Director of Data Science & Analytics to build statistical models, reports, and analytical tools that will increase our operational efficiency and drive incremental revenue. A key focus of this role will be on monetization and pricing of our marketplace, but the role also encompasses supporting a variety of business stakeholders, including Sales, Operations, Finance, and certain product lines internationally and in the US. This is a highly technical position where data science, statistics, and machine learning play a major role, but knowledge of analytics and data engineering are also required. The Director will report to the Head of Data and will be responsible for growing and setting the strategy and vision for their team.

RESPONSIBILITIES

• Manage, grow, and mentor a global team of data scientists and analysts.
• Inform our pricing strategy and develop algorithmic methods to optimize product pricing across different countries, markets, and categories.
• Build statistical and machine learning models to solve core business problems, such as pricing optimization, elasticity modeling, customer churn, lead scoring, lifetime value, and recommender systems.
• Build a reporting stack tailored to the stakeholder needs, including high-level executive reports and domain-specific operational reports.
• Lead vendor segmentation efforts to generate insights that can inform our sales and acquisition strategy.
• Improve upon existing methodologies for measuring marketplace performance in a subscription-based plan scenario.
• Explore and experiment with alternative auction-based monetization plans, including pay-per-lead and pay-per-booking approaches.
• Think holistically about the data needs of the organization across data science & analytics, business intelligence, machine learning, and data engineering.
• Design and conduct A/B testing experiments and advocate for an iterative learning culture.
• Develop tools and data products that automate manual work, increase operational efficiency, and empower users via self-service.

SUCCESSFUL CANDIDATES HAVE

• Experience with implementing pricing optimization and elasticity models with demonstrable business impact.
• Advanced degree in statistics, data science, operations research, engineering, or computer science (PhD highly desired).
• Excellent communicator with ability to translate technical concepts to non-technical audiences.
• Strong SQL and Python programming skills.
• Strong knowledge of applied statistics and machine learning.
• Experience working at a two-sided marketplace or eCommerce company.
• High-level understanding of data architecture, data pipelines, and database design.
• Knowledge of visualization and proficiency in at least one visualization tool.
• 4 years of data science leadership.
• Strong track record of influencing the organization and driving tangible business impact.

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ABOUT THE ROLE AND OUR TEAM

TKWW is looking for a VP of Data Platform to manage all its data infrastructure and shared services. This role requires a combination of cloud data technology and data governance expertise. The VP of Data Platform will be responsible for setting the vision and strategy for our data architecture across Data Engineering, Business Intelligence, and MLOps. They will collaborate with our Software Engineering partners on implementing technical solutions and build self-service capabilities that democratize analytics in the organization. Our ideal candidate is passionate about technology, forward thinking, and has a strong drive for innovation.

RESPONSIBILITIES

• Manage, grow, and mentor a team of engineers across Business Intelligence, Data Engineering, and MLOps.
• Design and implement a self-service platform to speed up development and automate the deployment and scaling of ML models in production. 
• Architect and manage the infrastructure to support all stages of the machine learning lifecycle, including feature engineering, feature store, model training, testing, monitoring, and deployment in a production environment. 
• Build a data warehouse architecture with a strong foundation in data modeling principles (e.g., dimensional modeling, data vault), while also allowing for rapid iteration and quick data access turnaround times.
• Build a semantic layer within the data warehouse where metrics can be defined once and reused across reporting tools and dashboards.
• Make analytics at TKWW fast, low cost, and simple, and build self-service capabilities that democratize data within the organization.
• Implement a portfolio of data management and governance capabilities, including metadata collection, data documentation, data lineage, data discovery, data quality, and data observability.
• Develop a reporting strategy that properly balances the speed of self-service capabilities and distributed development against the benefits of data governance and centralized control.
• Establish high standards and best practices across the team, including architecture design reviews, CICD processes, and database naming conventions.
• Manage event tracking platforms for robust utilization across the organization and deploy governance processes for tracking and tagging implementation.
• Drive a strong culture of iterative learning and architectural innovation that challenges the status quo.

QUALIFICATIONS

• Bachelor’s degree in Computer Science, Engineering, Data Science or related field (Master’s or PhD preferred).
• 10+ years leading data platform / engineering teams and experience building big data pipelines using Python, SQL, and Apache Spark. 
• 5+ years deploying and maintaining ML models in production with demonstrable business impact. 
• 5+ years supporting a business intelligence function with familiarity in at least two BI tools (e.g. Looker, PowerBI, Tableau, Qlik, etc.).
• Deep experience with event tracking management and analytics tools, such as Segment, Google Tag Manager, Google Analytics, and Mixpanel.
• First-hand experience with the modern data stack (Snowflake and DBT experience is strongly preferred).
• Broad understanding of cloud architecture tools and services, such as S3, EMR, Kubernetes, Lambda functions, and cloud data warehouses (prior AWS experience is highly desirable).
• Experience building streaming pipelines using Kafka and Spark or similar technologies.
• Knowledge of open-source data orchestration tools such as Apache Airflow, Luigi, Dagster, or Prefect and experience applying them. 
• Extensive experience designing and building data lakes in the cloud. 
• Experience architecting and building an enterprise data warehouse with hands-on knowledge of dimensional modeling. 

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