Senior Data Scientist

KikoffSan Francisco, CA
$226,000 - $254,000

About The Position

As a Senior Data Scientist at Kikoff, you work closely with cross functional teams incl. Product, Engineering, Design, Data Engineering, Marketing, Operation. By applying your technical skills, analytical mindset, and product intuition, you will help our customers improve their financial security. You will use data to identify and solve product development’s biggest challenges. Your responsibilities includes: Product leadership: use data to shape product development, identify new opportunities, set goals, quantify upcoming challenges, and ensure the products we build bring value to our customers. Measurement excellence: define what success means to our users and our business, build scalable framework to measure them. Technical ownership: develop hypotheses and employ a diverse toolkit of rigorous analytical approaches, causal inference / ML methodologies, and experimentation best practices to validate them. Communication and influence: convince and influence your partners by telling clear data stories.

Requirements

  • Bachelors’ or above in quantitative discipline: Statistics, Applied Mathematics, Economics, Computer Science, Engineering, or related field
  • A minimum of 5 years of work experience in analytics
  • Experience leading data science for an ambiguous and fast-changing product or technical area, working with senior partners in leadership, product, engineering
  • Expert knowledge of SQL and experience with Python
  • Deep understanding of statistical analysis, experimentation design, common ML and causal inference techniques
  • Excellent verbal and written communication skills
  • A humble collaborative can-do attitude and bias to action

Nice To Haves

  • Enthusiasm and comfort with using AI in analytics or for fintech problems
  • Experience with Machine Learning especially in a production environment

Responsibilities

  • Product leadership: use data to shape product development, identify new opportunities, set goals, quantify upcoming challenges, and ensure the products we build bring value to our customers.
  • Measurement excellence: define what success means to our users and our business, build scalable framework to measure them.
  • Technical ownership: develop hypotheses and employ a diverse toolkit of rigorous analytical approaches, causal inference / ML methodologies, and experimentation best practices to validate them.
  • Communication and influence: convince and influence your partners by telling clear data stories.
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