Senior Business Data Scientist, Machine Learning

Key Skills

Java Programming, Python Programming, C++, C Programming, SQL, Apache Hadoop, Scala Programming, Machine learning techniques, Data science techniques, PyTorch, TensorFlow, MapReduce, R Programming

Job Description

Minimum qualifications:

• 8 years of industry experience with 5 years of experience in machine learning or software engineering.
• Experience working in a cross-functional setting, managing the full lifecycle of projects as well as coaching members in managing their own projects.
• Industry experience with statistical software (e.g., Python, R), and database languages (e.g., SQL).

Preferred qualifications:

• Master's degree or PhD in a quantitative discipline (e.g., Statistics, Computer Science, Math, Engineering) or equivalent consulting experience.
• Experience working well with ambiguity, prioritizing needs, and delivering results in a dynamic environment.
• Knowledge and direct experience with both supervised and unsupervised learning (e.g., time series forecasting, clustering, classification) and/or Big Data techniques (e.g., Spark, Pig, Hive).
• Effective written and verbal communication skills to translate technical solutions and methodologies to executive leadership.

About the job

The Finance Data and Analytics (DnA) team combines business acumen, technology, and innovation to organize data, enable insights, and creates a data driven and efficient Finance organization in Google. As part of this team, you will have an opportunity to gain perspectives on Google’s core businesses, services, and the products.

At Google, data drives all of our decision-making. As a Senior Business Data Scientist, Machine Learning, you will work on strategic business challenges across multiple business areas (e.g., Ads, YouTube, Search, Google Play, etc.) through the lens of business growth. You will collaborate with data engineers, analysts, and product managers to create data solutions to enable our finance partners to make informed decisions, manage risks and opportunities.

In this role, you will communicate information and drive progresses for all aspects of a project’s delivery. As part of DnA growing data science team, you will advance your data science skills, support the data science development practices of the team, and collaborate closely with others.

The name Google came from googol, a mathematical term for the number 1 followed by 100 zeros. And nobody at Google loves big numbers like the Finance team when providing in depth analysis on all manner of strategic decisions across Google products. From developing forward-thinking analysis to generating management reports to scaling our automated financial processes, the Finance organization is an important partner and advisor to the business.

Responsibilities

• Partner with Finance leadership and their teams to understand business context, iterate and improve on data insights and solutions to deliver actionable information to the business.
• Apply statistical and machine learning methods to solve large-scale problems, including the full modeling lifecycle (e.g., data manipulation, building and evaluating models, interpreting and effectively communicating results, transitioning solutions to production, performance monitoring, recalibrating models, etc.).
• Create data visualization to summarize the modeling insights and communicate them to internal team members and business stakeholders.
• Continue to widen and deepen data science and engineering skill sets.
• Coach members, share knowledge, and support the continuous development of our tooling within the broader team.