Chief Data Scientist
– New York / Remote (US) (Competitive Salary Equity) The Company DataLogic is proud to partner with a fast-growing research platform that prides itself on maintaining a family-like small team culture in a high-energy, high-fun environment.
Connecting the insights of 5000 high-level IT Decision makers, representing over $1 trillion in annual IT spend, to the world’s top business leaders and investors.
They believe that “Opinions Only Exist Due to a Lack of Data.” For candidates looking for a company where they can reach their full potential, push the limits of their career, and surround themselves with curious, driven, and caring colleagues, they offer the dynamic energy and “Day 1” mindset of an early-stage venture, the stability and validation of decade-long client relationships with some of the world’s largest investors and corporations, and the opportunity to be on the front lines building a new type of research ecosystem in an industry ripe for disruption.
Role Summary The Chief Data Scientist, reporting directly to the CEO, sits at a unique crossroads between the data teams and the rest of senior management.
As a result, the Chief Data Scientist needs to be able to bridge the gap between business objectives (from initial strategy planning to reporting on KPIs) and data projects.
You will set the vision and build the roadmap for all things data.
You will lead and coordinate the efforts across data science and data analysis to coordinate with your colleagues from sales, product, technology, and content to produce a best-in-class research product.
Your mission is to continually ask and answer the question “How can we create additional value through the utilization of our data assets and analytics?” Responsibilities Create the strategy, build the team(s), and execute the roadmap for all data
– and quantitative related responsibilities including: Synthesizing and analyzing millions of data points from our ITDM community to find patterns, insights, and anomalies Using rigorous statistical testing to help refine and improve methodology for the company’s core survey product, including alternative delivery methods and optimal length and frequency to boost response rates Building visualization tools to help clients derive the maximum insights from our data o Exploring, in partnership with the research team, how to optimize the qualitative side of the research process, including potential areas for automation, NLP, sentiment analysis and machine learning Developing predictive and signal models that validate the value of company data Oversee and continuously improve our survey design and data collection activities, ensuring our products consistently exhibit rigorous and best-in-class survey practices Serve as an active member of our client-facing team, engaging first-hand with Voice of Customer feedback, brainstorming new products and solutions, and problem-solving with your data science counterparts at our clients Mentor and develop the members of the data team(s), guiding them through the execution of their duties and contributing to their professional growth Requirements: Advanced degree in Mathematics, Statistics, Computer Science, Physics, Engineering, or related discipline Professional experience using analytical concepts and statistical techniques, including: Developing hypotheses, Designing tests/experiments, Analyzing data, Testing for statistical significance, Drawing conclusions, Communicating actionable recommendations Strong understanding of survey methodologies, quantitative and qualitative research, and survey design principles A mix of executive presence and commercial awareness that allows you to confidently engage with, get feedback from, and propose ideas to our highly demanding clients Track record of building high-performing data science/data analytics teams Experience working with sales, product, and/or technology teams Fluency in Python or R and Source Control (e.g., GitHub) Experience with data visualization tools (we currently use Microsoft PowerBI but are exploring alternative Business Intelligence platforms) Knowledge of the financial services, markets, and trading are preferred (but not required)