Title: Staff Data Scientist
Salary: Up to $170,000 + Bonus, benefits & LTIP's
Forsyth Barnes are partnered with an exciting Retailer which is currently one of the top names in the industry, generating over $18 billion in revenue annually. As a business they heavily rely on Data Science and Machine Learning to provide valuable insights to power online and in-store business units.
Within the Data Science team, you will be a leader driving data analytics to create actionable insights improving the overall value of the business. Working closely with key stakeholder's across the organization, you will educate and drive data analytics initiatives.
Utilizing data & analytical tools, you will lead on product design and decisions, setting goals to manage and define processes.
Identify continuous improvements of key business metrics in specific business functional areas.
Translate outputs from analytical processes and convert into business outcomes, exchanging complex maths to tangible business outcomes.
Work closely with key stakeholders to drive business optimization via the data outcomes.
Build and validate models including, development, documentation and roll out of the Data Science lifecycle.
Mentor and support other junior Data Science/Analytics employees – not management.
Deep understanding of Machine Learning algorithms, statistical and optimization methods all aligned with key stakeholder's needs and business growth.
Experience working cross functionally with product management, design, data and engineering teams.
Technical experience utilizing tools and languages such as Python, R, Spark, SQL, BigQuery.
Experience with fast moving environments/businesses including new experiments proposed, tested and implementation of new strategies where needed.
Previous experience in the Retail/Ecommerce industries and/or Supply Chain Management would be ideal for this position.
401K, potential of LTIP's (Depending on location), Healthcare & Dental and more.
This opportunity does not accommodate any type of sponsorship at this time, apologies.