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The Decision Science team drives data-driven strategy and decision making across the company through predictive analytics, testing and optimization. We work on a wide variety of problems by proactively identifying opportunities. We drive product prioritization and inform business strategy by developing a deep understanding of our users and clients. We work with Product team to build and refine data products, such as personalization to power the site and internal products, such as fraud detection, to improve our business efficiencies. We work with Sales team to score and prioritize leads, with Marketing team to build media models, optimize spend to increase pipeline, with Marketplace to understand elasticity of supply and demand, and work with Finance and Business Operations teams on financial planning and forecasting. If gaining exposure to a wide variety of business functions across a fast-growing company excites you, this is the place to be.
The Head of Consumer Analytics will head up a team of 5-6 data scientists working with the Consumer team.
Our product philosophy is to build products that solve user pains. We are relentless in learning through experimentation. Since different job seekers have different needs, every feature we build must be contextual and personalized. Data informs every feature we build. We deeply analyze user behavior, through our logs or external research.
As the Head of Consumer Analytics you will support the Consumer Product and Consumer Marketing teams. The Consumer Products includes Jobs, Content (Salary/Ratings/Reviews/Benefits/etc.), Engagement, International, and Mobile App teams. The Consumer Marketing team includes Brand Marketing and Public Relations teams. You will drive deep and holistic understanding of our users, for example: What drives users engagement? What are the drivers of repeat visits? What is the optimal frequency and relevancy threshold for job alerts? What is the LTV of a user across various channels? How should we track and measure TV and radio campaigns? What should be our mobile app and mobile web strategy? Which new features and products should we be working on? You'll generate a number of hypothesis and inform decision making for all these questions. In addition, you’ll define detailed data requirements and prioritize them with the Data Engineering team and ensure that data solutions developed are intuitive and meet the needs of the business users.
A typical week would comprise of brainstorming with Product and Marketing Directors on new project ideas, building strategy and criteria for success of the project, sprint planning, KPI measurement, building dashboards for business teams to consume, analyzing the health of our product, developing insights and recommendations based on deep dive analyses.
The ideal candidate will be extremely results driven and passionate about winning. You exercise very sound judgment and have the ability to balance sophistication with simplicity, scientific rigor with pragmatism and agility with quality.
6+ years of quantitative experience, ideally in an Internet company. Quantitative Degree – like Statistics, Operations Research, Physics, Computer Science, Economics, etc.
Leadership: Ability to partner with executives, business stakeholders and product managers to define roadmaps. You see what could be and drive the organization there
People Management: You must have experience hiring, mentoring, and managing a team of data scientists with high retention rates
Strong applied analytics: This means you love data, gleaning insights from data, and making decisions based on data. You need to have demonstrable history of doing this
Outstanding problem solving skills: You’re not just repeating a playbook, you’re looking at every problem with fresh eyes and digging deep for insights and solutions
Experience with techniques and tools: Hands-on experience working with large data-sets, modeling, and statistics. Proficiency at SQL, R or Python. Extensive A/B & multivariate testing experience.
Head of Consumer Analytics - Data Science/AI/Machine Learning
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Job Info
| Category | Data Science/AI/Machine Learning |
| Employment Type | Full-Time Employment |
| Compensation | $170000.00 - $188000.00 |
| Location | United States, CA - 94111 |
Client Introduction
The Decision Science team drives data-driven strategy and decision making across the company through predictive analytics, testing and optimization. We work on a wide variety of problems by proactively identifying opportunities. We drive product prioritization and inform business strategy by developing a deep understanding of our users and clients. We work with Product team to build and refine data products, such as personalization to power the site and internal products, such as fraud detection, to improve our business efficiencies. We work with Sales team to score and prioritize leads, with Marketing team to build media models, optimize spend to increase pipeline, with Marketplace to understand elasticity of supply and demand, and work with Finance and Business Operations teams on financial planning and forecasting. If gaining exposure to a wide variety of business functions across a fast-growing company excites you, this is the place to be.
Job Description
The Head of Consumer Analytics will head up a team of 5-6 data scientists working with the Consumer team.
Our product philosophy is to build products that solve user pains. We are relentless in learning through experimentation. Since different job seekers have different needs, every feature we build must be contextual and personalized. Data informs every feature we build. We deeply analyze user behavior, through our logs or external research.
As the Head of Consumer Analytics you will support the Consumer Product and Consumer Marketing teams. The Consumer Products includes Jobs, Content (Salary/Ratings/Reviews/Benefits/etc.), Engagement, International, and Mobile App teams. The Consumer Marketing team includes Brand Marketing and Public Relations teams. You will drive deep and holistic understanding of our users, for example: What drives users engagement? What are the drivers of repeat visits? What is the optimal frequency and relevancy threshold for job alerts? What is the LTV of a user across various channels? How should we track and measure TV and radio campaigns? What should be our mobile app and mobile web strategy? Which new features and products should we be working on? You'll generate a number of hypothesis and inform decision making for all these questions. In addition, you’ll define detailed data requirements and prioritize them with the Data Engineering team and ensure that data solutions developed are intuitive and meet the needs of the business users.
A typical week would comprise of brainstorming with Product and Marketing Directors on new project ideas, building strategy and criteria for success of the project, sprint planning, KPI measurement, building dashboards for business teams to consume, analyzing the health of our product, developing insights and recommendations based on deep dive analyses.
Job Responsibilities
Experience
Required Experience
The ideal candidate will be extremely results driven and passionate about winning. You exercise very sound judgment and have the ability to balance sophistication with simplicity, scientific rigor with pragmatism and agility with quality.
6+ years of quantitative experience, ideally in an Internet company. Quantitative Degree – like Statistics, Operations Research, Physics, Computer Science, Economics, etc.
Leadership: Ability to partner with executives, business stakeholders and product managers to define roadmaps. You see what could be and drive the organization there
People Management: You must have experience hiring, mentoring, and managing a team of data scientists with high retention rates
Strong applied analytics: This means you love data, gleaning insights from data, and making decisions based on data. You need to have demonstrable history of doing this
Outstanding problem solving skills: You’re not just repeating a playbook, you’re looking at every problem with fresh eyes and digging deep for insights and solutions
Experience with techniques and tools: Hands-on experience working with large data-sets, modeling, and statistics. Proficiency at SQL, R or Python. Extensive A/B & multivariate testing experience.
