Salesforce.com, Inc Lead Data Scientist in Burlington, Massachusetts
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Products and Technology
Department Overview: Strategy & Growth (SnG)
The Data Intelligence (Di) team is made up of data scientists, engineers, and analysts who are dedicated to driving product and distribution strategy with data-driven insights. The Di team works with executives, product managers, designers, developers, user researchers, marketers, customer success, and sales team members across all Cloud businesses to discover new opportunities for growth and optimization, drive data-informed product strategy, and create data product experiences and actionable insights that enable our stakeholders to maximize product adoption and revenue. This role will lead the Cloud Grow Pillar, reporting directly to the VP of Strategy & Growth.
Salesforce.com builds the nerve centers that allow the fortune 500 and small businesses alike to seamlessly manage their customer relationships, housing one of the worlds largest and richest B2B datasets. The Di team is a family of multi-talented technologists with backgrounds ranging from Artificial Intelligence to Finance. What connects us all is a shared curiosity for how to drive product strategy with data, a passion for getting the job done, and a healthy disregard for constraints.
What You Will Do:
As a Lead Data Scientist, you will become a thought partner to the senior leadership of a specific product or initiative, and work on enterprise analytics problems that require tremendous creativity. Your goal will be to help shape Salesforce products by delivering insights, conducting adoption tests, and guiding a team of engineers and data scientists to productize these insights. This position will report to one of the leaders of the Strategy & Growth team. Successful candidate will need to:
Partner with Senior Leadership (VP+) to understand their business and advise on strategic objectives, product direction, roadmaps, growth goals, retention strategies, etc. Develop and own the relationships with senior stakeholders across the company.
Produce insights (e.g. performance drivers, retention analysis, behavioral personas) to help grow Salesforce Cloud businesses. This requires acquiring, cleaning, structuring data from multiple sources (e.g. Hadoop, Splunk), and analyzing the data using SQL / Python / R.
When analyses or data science models are ready to be productized, own the delivery by working with engineers and data scientists to turn insights into self-service dashboards or data products.
Evangelize the insights through storytelling. Create easy-to-consume media that inspires into action anyone from senior execs to fellow analysts.
Contribute to expanding the Salesforce data culture by growing new relationships, hosting learning sessions, integrating or designing new tools, improving team processes, and other lateral activities.
Senior or lead data analyst or data scientist with at least 8-10 years of experience.
Masters degree or doctorate in mathematics, statistics, computer science, or related field.
Strong working knowledge (including ability to review and comment) of statistical and machine learning techniques, their advantages, disadvantages and areas of application.
Build, validate, and tune the statistical and ML models to achieve desired outcomes. Be able to make tradeoffs between parameters such as speed and accuracy.
Who You Are:
Thought leader in analytics (more than 5-10 years of experience in product analytics, sales analytics, marketing analytics or similar, as IC or people manager).
Laser focused on impact, balancing effort to value, and getting things done.
Curious, creative, opinionated thinker with a talent for detecting patterns and elevating through strategy.
Charismatic storyteller ready to lead growth conversations with senior leadership.
Expert in building relationships and collaborating in matrixed environments.
Able to make intelligent decisions on the right methodology for the problem to be solved.
Capable of building the appropriate metrics framework and identifying key data assets needed for a particular product(s)/cloud(s), address gaps and foresee needs. Awareness and utilization of best practices of peripheral products(s)/cloud(s).
Ability to think through the data architecture needed for an analytical project, making recommendations to engineering about how to structure datasets, and demonstrating an expert grasp of tradeoffs of different data structures and pipeline options.
Expert knowledge of data sources, data pipelines, and database technologies for the product areas responsible for, as well as cross-cloud data assets, instrumentation best practices, and databases.
Ability to assist in creating a Big Data environment as well as capturing and evaluating results in a big data setting.
Own the metrics and data quality for the responsible areas/products. Proactively seek ways to improve data quality, efficiency and scalability.
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