Data Science, Investment Research
Full time /
As a member of the Data Science team, you are responsible for leveraging Drop's proprietary data to surface insights that drive impactful solutions for key stakeholders both internally and externally.
You will have the opportunity to collaborate with the Product, Engineering, Data Engineering, Marketing, Partnerships, Finance teams and other Data Scientists. Your work will have a direct impact on Drop’s growth trajectory and future success.
You can learn more about our Data & Engineering team’s work by visiting Drop’s Engineering Blog.
As a Data Scientist you will:
- Work closely with the Data Partnerships, Product, Infrastructure and Client Success teams to support and evolve our suite of alternative data products
- Conduct exploratory analysis across a growing cc/debit transaction dataset to identify trends and patterns
- Collaborate with internal and external stakeholders to brainstorm solutions that drive key strategic initiatives
- Act as a business partner leveraging your strong understanding of our data to further develop our data product offering and expand our product suite
- Engage with potential clients as a technical counterpart to the Data Partnerships team
- Collaborate with stakeholders to clearly define problems we want to tackle, brainstorm solutions, conduct quantitative analysis, and data mining, and present the findings to stakeholders in order to achieve our goals together
- Be responsible for technical maintenance of our warehouse to ensure data quality for stakeholders across the company
- Develop dashboards within our business intelligence tool and act as the data expert to promote a culture of data-informed decision-making at Drop
A Little Bit About You:
- Industry experience in data science
- Hedge Fund or investment management experience a must
- Client Facing experience with strong communication skills. The ability to gather requirements from clients, translate them into an analytic problem, and share results using our data products in a clear and concise manner
- Experience working with SQL to develop complex queries within large data sets
- Experience in designing experiments and coming up with scientifically sound recommendations
- Strong analytical and problem-solving skills; you are able to transform data into actionable product and business insights
- Experience querying and transforming structured and semi-structured data sets
- Experience leading cross-functional collaboration and are comfortable with undefined and vague business problems.
- Strong communication skills, with the ability to gather requirements from stakeholders, translate them into an analytical problem, and share outcomes clearly and concisely
- You are passionate about building the next-generation loyalty product to make life more rewarding
Bonus Points If:
- Experience with Looker, Redshift, Postgres, Athena, Airflow, Python or Snowflake
- You have experience with scripting languages such as Python or R
- You’ve worked with Looker or Tableau
- You have experience working directly with clients
- Experience with transactional data
- Experience with product and business analytics
- Experience with financial, loyalty, or rewards systems
- Experience at a consumer tech start-up is a bonus. Drop welcomes people from all backgrounds and recognizes the value of diversity
- You have a degree in Statistics, Math, Computer Science, Engineering or related fields
At Drop, we're committed to providing an enjoyable and meaningful environment for every member of our team. We operate under a flat structure with minimal hierarchy where everyone’s opinion is valued equally. We are looking for team members with an entrepreneurial mindset who will thrive in a fast-paced and rewarding environment.
Drop Technologies, Inc. is proud to be a diverse and equal opportunity employer and as such does not discriminate on the basis of race, colour, religion, sex, national origins, age, sexual orientation, disability or any other characteristic protected by applicable laws. Selection decisions are solely based on job-related factors.