Product Data Scientist, GTE Data Science and ML
Google · Hyderabad, Telangana, India
- On site
- Full-time
- $150,000 / year
- Hyderabad, Telangana, India
Job highlights
- Analyze data to drive product decisions and recommendations.
- Develop machine learning models for business impact.
- Collaborate with Engineering and Product Management teams.
- Transform enterprise operations with AI and analytics.
- Advance AI product performance using ML techniques.
About the role
About The Job
Help serve Google's worldwide user base of more than a billion people. Data Scientists provide quantitative support, market understanding and a strategic perspective to our partners throughout the organization. As a data-loving member of the team, you serve as an analytics expert for your partners, using numbers to help them make better decisions. You will weave stories with meaningful insight from data. You'll make critical recommendations for your fellow Googlers in Engineering and Product Management. You relish tallying up the numbers one minute and communicating your findings to a team leader the next.The Googler Technology and Engineering (GTE) team partners with teams across the company to apply Google’s best Data Science techniques to Google’s biggest enterprise opportunities. We partner with Research, Core Enterprise Machine Learning (ML) and ML Infrastructure teams to build solutions for our enterprise.
The GTE Data Science team's mission is to:
- Transform Google Enterprise business operations, supply chain, IT support and internal tooling with AI and Advanced Analytics
- Enable operations and product teams to succeed in their advanced analytics projects through the use of differing engagement models, ranging from consulting to productionizing and deploying models
- Build cross-functional services for use across Corporate Engineering
- Educate product teams on advanced analytics and ML
Responsibilities
- Define and report key performance indicators and launch impact as part of regular business reviews with the cross-functional and cross-organizational leadership team.
- Translate analysis results to business insights or product improvement opportunities.
- Develop hypothesis to enhance performance of AI products on offline and online metrics through research on techniques around prompt engineering, RAG, supervised finetuning, in-context learning, dataset augmentation, tool-calling efficacy, planning capabilities and feedback loop with reinforcement learning.
- Design and develop ML strategies for data enrichment such as autoencoder based latent variables, complex heuristics etc.
- Evolve variance reduction and simulation strategies to increase reliability of experiments with small sample sizes.
- Unlock continually improving experimentation with algorithms like contextual bandits.
- Convert business problems into unsupervised and supervised machine learning modeling problems, and build these model prototypes from scratch to justify business impact hypothesis.
Minimum qualifications
- Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
- 10 years of experience using analytics to solve product or business problems, performing statistical analysis, and coding (e.g., Python, R, SQL) or 8 years of experience with a Master's degree.
Preferred qualifications
- Experience with developing at least one deep learning or conventional machine learning model for business impact.
- Experience debugging throughput, latency and response quality issues in AI products, from an analytical perspective.
- Experience managing large-scale data transformation pipelines for batch inference of ML models.
Key skills/competency
- Data Science
- Machine Learning
- Product Analytics
- Statistical Analysis
- Python
- R
- SQL
- Deep Learning
- AI Products
- Experimentation
Skills & topics
- Product Data Scientist
- Data Science
- Machine Learning
- AI
- Analytics
- Python
- R
- SQL
- Deep Learning
How to get hired
- Tailor your resume: Highlight your 10+ years of experience in analytics, statistical analysis, and coding (Python, R, SQL) or 8 years with a Master's degree. Emphasize ML model development and debugging AI product issues.
- Showcase impact: Quantify your achievements in previous roles, demonstrating how your analytical insights led to product improvements or business success.
- Prepare for technical questions: Be ready to discuss your experience with deep learning, ML model development, large-scale data pipelines, and debugging AI product performance.
- Understand Google's culture: Research Google's mission, values, and the GTE team's focus on applying AI to enterprise opportunities.
- Network and engage: Connect with current Google employees on platforms like LinkedIn to gain insights into the company and the role.
Technical preparation
Master Python, R, and SQL for data analysis.,Practice building ML models (deep learning/conventional).,Study AI product debugging for latency/quality.,Review large-scale data pipeline management techniques.
Behavioral questions
Describe a complex business problem you solved.,How do you translate data into actionable insights?,Tell me about a time you influenced a decision.,How do you collaborate with cross-functional teams?
Frequently asked questions
- What are the key responsibilities for a Product Data Scientist at Google?
- As a Product Data Scientist at Google, you will define and report on key performance indicators, translate analysis results into business insights, develop hypotheses to enhance AI product performance using various ML techniques, design ML strategies for data enrichment, and build ML model prototypes from scratch to justify business impact.
- What educational background is required for the Product Data Scientist role at Google?
- A Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field is required. Alternatively, 8 years of experience with a Master's degree in a relevant field is also accepted.
- What kind of experience is preferred for this Google Data Scientist position?
- Preferred qualifications include experience developing at least one deep learning or conventional machine learning model for business impact, debugging throughput, latency, and response quality issues in AI products, and managing large-scale data transformation pipelines for ML model inference.
- How does the GTE Data Science team contribute to Google's enterprise goals?
- The GTE Data Science team's mission is to transform Google Enterprise business operations, supply chain, IT support, and internal tooling with AI and Advanced Analytics. They also enable operations and product teams through advanced analytics projects and build cross-functional services.
- What programming languages and tools are essential for a Product Data Scientist at Google?
- Proficiency in coding languages such as Python, R, and SQL is essential. Experience with statistical analysis and performing data analysis to solve product or business problems is also crucial.
- How can I best prepare my resume for a Product Data Scientist application at Google?
- Your resume should clearly detail your extensive experience in analytics and quantitative fields, specifically highlighting your ability to solve product or business problems. Emphasize your skills in statistical analysis, coding (Python, R, SQL), and any experience with machine learning model development and deployment. Quantify your impact whenever possible.
- What are Google's data scientists expected to do regarding AI product improvement?
- Data scientists are expected to develop hypotheses for enhancing AI product performance on various metrics. This involves researching techniques like prompt engineering, RAG, supervised finetuning, in-context learning, and integrating feedback loops with reinforcement learning.