Data Science Engineer, iCapital

• Ideal candidates will be able to understand complex business problems, interface with internal stakeholders, develop execution plans, implement all aspects of the project technically, and finally present on their work
• Bachelor’s degree or higher in Computer Science, Economics, Mathematics, Statistics or a related technical field
• 4 to 8 years of experience in an data-related role
• Excellent knowledge of SQL (dbt experienced preferred), Python and pandas
• Very good knowledge of statistics, ML models and ML services/infra
• Good knowledge of data modeling, relational databases, normalization, OLAP stores
• Good writing, communication and presentation skills
• Familiar with a business intelligence tool (e.g. Tableau, Looker, PowerBI)

Desirable

• We are looking for a Data Science Engineer, Assistant Vice President to measurably drive growth for our business teams using analytics, data science and machine learning
• In this role, you will work with our Sales, Marketing and Product groups to define, calculate and grow their key operating metrics (e.g. sales conversion, marketing conversion, product adoption, CAC/CLTV)
• You will sit in the Analytics group of the broader Chief Data Office (CDO) team and work closely with our Business Intelligence, Data Engineering and Machine Learning teams
• Day-to-day, you will requirements gather from internal stakeholders, conduct exploratory data analyses, perform statistical inference, and deploy machine learning models to production. You will be our subject matter expert when it comes to data science and machine learning
• At a technical level, you will build data sets using dbt/SQL, visualize measures of success using Tableau, formulate exploratory data analyses using pandas/Jupyter, develop statistical and machine learning models using Python, and finally deploy production-ready code using GitLab
• This is a technical, individual contributor role that blends data engineering, data analytics, data science and machine learning
• Write Python and SQL (in dbt) to extract, transform, validate, and aggregate data
• Conduct exploratory data analyses, build machine learning models and develop ML infra in Jupyter and Python
• Develop statistical models and construct data-driven experiments (e.g. A/B tests)
• Visualize key metrics using Tableau
• Work closely with our engineering, product and business teams to form a thorough understanding of our industry and evolving data model