Data Scientist, Product Analytics - Machine Learning at Meta in New York, NY

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Job Description

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 As a Machine Learning Data Scientist at Meta, you will have the opportunity to do groundbreaking applied machine learning work that will shape the industry and the future of people-facing and business-facing products we build across our entire family of applications (Facebook, Instagram, Messenger, WhatsApp, Reality Labs). By applying your Machine Learning knowledge and technical skills, analytical mindset, and product intuition to one of the richest data sets in the world, you will help define the experiences we build for billions of people and hundreds of millions of businesses around the world. You will collaborate on a wide array of product and business problems with a diverse set of cross-functional partners across Product, Engineering, Research, Data Engineering, Marketing, Sales, Finance and others. You will use data and analysis to identify and solve product development&rsquo;s biggest challenges in ML systems through insights as well as prototyping ML solutions. You will influence product strategy and investment decisions with data, be focused on impact, and collaborate with other teams. By joining Meta, you will become part of a world-class analytics community dedicated to skill development and career growth in analytics and beyond. In contrast to most ML engineering roles, ML in product analytics allows you to work out ML solutions for broader less defined problems where you can use not just ML knowledge but also strong analytical skills to break down complex problems into well-learnable parts.Product leadership: You will use data to shape product development, quantify new opportunities, identify upcoming challenges, and ensure the products we build bring value to people, businesses, and Meta. You will help your partner teams prioritize what to build, set goals, and understand their product&rsquo;s ecosystem.Analytics: You will guide teams using data and insights. You will focus on developing hypotheses and employ a diverse toolkit of rigorous analytical approaches, different methodologies, frameworks, and technical approaches including Machine Learning to test them. You will research challenging ML questions to inform experimentation and can build ML prototypes. .Communication and influence: You won&rsquo;t simply present data, but tell data-driven stories. You will convince and influence your partners using clear insights and recommendations. You will build credibility through structure and clarity, and be a trusted strategic partner.
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 Data Scientist, Product Analytics - Machine Learning Responsibilities:
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   Partner with cross-functional engineering and product teams to derive quantitative understanding of Meta&rsquo;s ML infrastructure and ML applications to inform future strategy and design ML solutions for complex problems.
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   Define, understand, and test opportunities and levers to improve the product through ML models and applications, and drive ML-modeling roadmaps through your insights and recommendations.
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   Build ML prototyping solutions.
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   Work with large and complex data sets to solve a wide array of challenging problems using different analytical and statistical approaches.
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   Apply technical expertise with quantitative analysis, experimentation, data mining, and the presentation of data to develop strategies for our products that serve billions of people and hundreds of millions of businesses.
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   Identify and measure success of product efforts through goal setting, forecasting, and monitoring of key product metrics to understand trends.
  </li>
  <li>
   Define, understand, and test opportunities and levers to improve the product, and drive roadmaps through your insights and recommendations.
  </li>
  <li>
   Partner with Product, Engineering, and cross-functional teams to inform, influence, support, and execute product strategy and investment decisions.
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  </li>
 </ul>
 Minimum Qualifications:
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  <li>
   A minimum of 6 years of work experience in analytics (minimum of 4 years with a Ph.D.) with a focus on one of the following: ML Modeling, Ranking, Recommendations, or Personalization systems.
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  <li>
   Bachelor's degree in Mathematics, Statistics, a relevant technical field, or equivalent practical experience.
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   Experience with applying machine learning techniques to big data systems (e.g., Spark and Hadoop) with TB to PB scale datasets.
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  <li>
   Experience with data querying languages (e.g. SQL), scripting languages (e.g. Python), and/or statistical/mathematical software (e.g. R).
  </li>
  <li>
   Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience.
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   <br/>
  </li>
 </ul>
 Preferred Qualifications:
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 <ul>
  <li>
   Masters or Ph.D. Degree in a quantitative field.
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  </li>
 </ul>
 About Meta:
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 Meta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps like Messenger, Instagram and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. People who choose to build their careers by building with us at Meta help shape a future that will take us beyond what digital connection makes possible today&mdash;beyond the constraints of screens, the limits of distance, and even the rules of physics.
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 Meta is proud to be an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law. Meta participates in the E-Verify program in certain locations, as required by law. Please note that Meta may leverage artificial intelligence and machine learning technologies in connection with applications for employment.
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 Meta is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance or accommodations due to a disability, please let us know at accommodations-ext@fb.com.
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 $173,000/year to $242,000/year + bonus + equity + benefits
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 Individual compensation is determined by skills, qualifications, experience, and location. Compensation details listed in this posting reflect the base hourly rate, monthly rate, or annual salary only, and do not include bonus, equity or sales incentives, if applicable. In addition to base compensation, Meta offers benefits. Learn more about  benefits  at Meta.
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AI Powered Job Insights

Exciting opportunity for a Machine Learning Data Scientist at Meta! They are seeking a skilled individual to take on a vital role in product analytics, utilizing machine learning techniques to influence and enhance the user experience across Meta's applications like Facebook, Instagram, and WhatsApp.

📍 Location: New York, NY  
💼 Position: Data Scientist, Product Analytics - Machine Learning  
⏰ Type: Full-time  
📅 Date Posted: 2024-07-25  

Role Summary:  
- Engage in groundbreaking applied machine learning to shape the direction of Meta’s products.  
- Collaborate across various teams, using data to solve complex product challenges.  
- Develop strategies and model applications that serve billions of users worldwide.  

What You'll Do:  
- Partner with cross-functional teams to enhance ML infrastructure and applications.  
- Define and assess opportunities to improve products through ML solutions.  
- Handle large datasets, applying diverse analytical techniques to address challenges.  
- Build ML prototypes and provide clear insights to influence product strategies.  
- Measure product success through metric tracking and goal-setting.  

What's Needed:  
- Minimum of 6 years in analytics (4 years with a Ph.D.), focusing on ML Modeling, Ranking, Recommendations, or Personalization.  
- Bachelor's degree in Mathematics, Statistics, Computer Science, or related fields.  
- Experience with big data systems like Spark and Hadoop.  
- Proficiency in SQL, Python, and statistical analysis tools.  

Preferred Qualifications:  
- A Master's or Ph.D. in a quantitative field, enhancing their technical expertise.  

Meta is dedicated to creating a diverse and inclusive workspace, ensuring equal opportunities for all candidates. Compensation ranges from $173,000/year to $242,000/year, supplemented by bonuses, equity, and benefits.

Top Interview Questions

  • Q: Can you describe a project where you successfully applied machine learning techniques to solve a product-related problem?

    A: In my previous role, I worked on a recommendation system for an e-commerce platform. By utilizing collaborative filtering and content-based filtering approaches, I analyzed large datasets to understand user preferences. I implemented the model using Python and Scikit-learn, which significantly increased user engagement by 25% and improved sales conversion rates. This experience honed my ability to translate complex ML solutions into actionable product insights.

  • Q: What strategies do you employ to communicate complex data analysis results to non-technical stakeholders?

    A: I focus on simplifying the analysis without losing the core essence. I use visualizations to present data insights clearly, employing tools like Tableau or Matplotlib for impactful storytelling. Additionally, I use everyday language and contextual examples relevant to the business objectives, ensuring that my recommendations are not just understood but actionable. For instance, in my last project, I presented KPI trends to the marketing team by correlating their actions to data changes, which helped in tailoring their strategies.

  • Q: How do you prioritize the various machine learning opportunities within a product team?

    A: I assess opportunities based on their potential impact and alignment with organizational goals. I collaborate with product and engineering teams to understand their current challenges and hypotheses for improvement. Using a scoring system that includes metrics like projected ROI, feasibility, and technical resources needed, I prioritize initiatives. For example, I once led a prioritization session where we identified a high-impact predictive maintenance project that reduced downtime by 30%, significantly improving operational efficiency.

  • Q: Discuss a time when your analysis had a significant impact on product strategy. What metrics did you monitor?

    A: During my time working on a social media application, I conducted a comprehensive analysis of user engagement metrics to understand drop-off points in the customer journey. I identified that users were relatively less engaged after their third week of usage. By suggesting enhancements in user onboarding and personalized content recommendations, we saw a 15% increase in user retention rates. I closely monitored metrics such as Daily Active Users (DAU) and churn rates to evaluate the success of the implemented strategies.

  • Q: What methodologies do you prefer when building ML prototypes, and why?

    A: I typically prefer the CRISP-DM framework for structuring my ML projects. It emphasizes understanding the business problem, data preparation, and iterative model training. For prototyping, I use Rapid Prototyping techniques with libraries like PyTorch or TensorFlow, focusing on developing MVPs (Minimum Viable Products) that answer specific business questions promptly. This allows for quick validation of ideas with stakeholders. For instance, during a recent project, I built a quick prototype that used decision trees, which helped us iterate rapidly based on initial feedback and adapt the model in subsequent phases.

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