Senior Business Data Scientist, Learning Services, Cloud

Minimum qualifications:

• Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, or equivalent practical experience.
  
• 4 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis.
  

Preferred qualifications:

• Master's degree in Computer Science, Statistics, Economics, Engineering or Mathematics.
• 5 years of experience in data science or quantitative analytics with focus on statistical modeling, machine learning, and AI.
• 5 years of experience in business visualization tools e.g., Looker, Tableau, etc.
• Experience working with large datasets in a distributed system and with SQL and Python.
• Experience with Google Cloud platform (GCP).
• Excellent technical skills with an ability to collaborate with technical partners and influence technical outcome of the project.

About the job

Google's leadership team hand-picks thorny business challenges, and members of BizOps work in small teams to find solutions. As part of this team you fully immerse yourself in data collection, draw insight from analysis, and then zoom out to develop compelling, synthesized recommendations. Taking strategy one step further, you also persuasively communicate your recommendations to senior-level executives, roll-up your sleeves to help drive implementation and check back-in to see the impact of your recommendations.

Cloud Learning Services (CLS) is revolutionizing cloud learning. We empower users of all levels with interactive labs and guided experiences to build practical skills on Google Cloud Platform and other leading technologies. Our mission is to make the cloud accessible, engaging, and enjoyable to learn.

As a Business Data Scientist, you will play a key role in uncovering valuable insights from various data sources to solve critical business issues. You will manage a range of exciting projects, from establishing new measurement frameworks to identifying meaningful patterns in large datasets, driving LLMs, ultimately empowering stakeholders to make data-driven decisions.

You will determine the most effective investigative approaches to address business problems and will clearly and effectively communicate your findings to decision-makers with varying technical backgrounds. You will influence data-driven decision-making across the organization and contribute to impactful outcomes. This role is for a detail-oriented and problem-solver with a strong foundation in data analytics, data visualization, and AI/ML. You will have excellent communication and stakeholder management skills as you will collaborate extensively with cross-functional teams.The US base salary range for this full-time position is $163,000-$237,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Responsibilities

• Extract insights from large datasets and build data products like dashboards to operationalize them, driving measurable improvements in key performance indicators (KPIs).
• Present and communicate insights and recommendations to executives, leaders, and cross-functional partners, including product, engineering, and marketing teams.
• Serve as a peer-reviewer and consultant to other members of the team, fostering a collaborative and knowledge-sharing environment.
  
• Drive the development and operationalization of AI/ML to enable data-driven decisions at scale.
• Learn and share knowledge of the latest advancements in AI/ML and data science that are relevant to our work.