Data Scientist

Job Description

The role will directly contribute to the SMB Early lifecycle metrics which ultimately translates to incremental revenue. Your day to day Experiment measurement Experiment design, ramp decisions, segment level insights & measurement Opportunity identification Entails solving open ended problems to uncover opportunities to move the key KPIs. Often involves ML/DS techniques RCA Uncover why the KPIs are fluctuating (up and down) or if some experience is broken and requires investigating Improve product experiences for 200+ million customers using data science techniques Leverage the Objective & Key Results (OKR) framework to identify product metrics and monitor product performance through dashboards and ad hoc analysis Present findings and recommendations to senior-level/non-technical stakeholders Deliver actionable, data-driven insights to help understand customer behavior, launch and optimize new features, and prioritize the product roadmap Build automation Identify key metrics, conduct rigorous explorative data analysis, create exec-facing dashboards, and build business cases that drive decision-making and business prioritization Support multiple projects at the same time in a fast-paced, results-oriented environment Requirements Data-driven mindset with a degree in a quantitative discipline such as Computer Science, Statistics, Engineering, Mathematics, or Economics 2+ years of relevant work experience in analyzing large, multi-dimensional data sets and synthesizing data insights into actionable recommendations Story telling & Communication. SQL (advanced) & Python (Basic) ML & Stats application AB testing measurements Exceptional written and verbal communication skills to influence cross-functional teams Prior work experience in digital (website/marketing/product) analytics, management consulting, investment banking, and/or consumer financial products is highly valued Experience/knowledge of employing statistical, data mining, and machine learning algorithms (e.g. regression and clustering) on real-world problems is a plus