Data Science Solutions Architect
Posted 4 days ago
What is Enterprise Data?
Bloomberg's Enterprise Data department develops data offerings that are considered best in class by the capital markets community. Across real time market data, reference data, historical pricing data, and unique analytics we offer:
- The most comprehensive and highest quality content in the industry
- Distribution platforms that are flexible, reliable, fast, and easy to onboard
- Easy to use data that is ready for analysis
These critical datasets serve as the primary source of information across the front, middle, and back office at the most respected capital markets firms across the globe.
What is the Role?
Capital markets firms are purposefully embracing data science and machine learning techniques into their workflows. Motivated by increasingly sophisticated competition or cost savings, data science and machine learning have become a critical aspect of our customers’ business strategies. Bloomberg wants to be the leader in analysis ready data that allows clients to focus on the business of creating advanced analytics solutions rather than data ingestion and normalization.
You will play a meaningful role in helping customers and Bloomberg, together, achieve success. As a hands on liaison between Bloomberg product development teams and the data science teams at our customers, the Solutions Architect will provide experienced technical design, data science thought leadership, and Bloomberg recommended standard methodologies as customers develop solutions on premises or in the public cloud.
The ideal candidate will be a customer focused data scientist with advanced technology skills that seeks opportunities to get their hands dirty while confidently working with clients to design and build solutions that will best demonstrate Bloomberg content and technology in conjunction with modern data science tools and workflows.
We'll Trust you to:
- Lead deep technical discussions with customers, vendor partners, and Bloomberg colleagues from Product, Sales, Quant Research & Development, Engineering, and Client Services
- Serve as subject matter experts in demonstrating advanced data science workflows and technologies for capital markets use cases
- Engage with customers as part of their solution creation team
- Confidently make recommendations (based on standard methodologies) to customers and partners
- Develop collateral including tutorials, sample code, reference implementations, and presentations that will be used by data science practitioners as well as executive decision makers
- Provide feedback to Product, Quant, and Engineering teams to help shape product strategy and execution roadmap
- Balance hands on work with a desire to keep up with trends
What do I need to apply?
- Experience with applying data science/quantitative modeling to real world, financial use cases commonly deployed at capital market firms
- Understanding of capital markets, banking, asset management, and/or the trade lifecycle
- Extensive knowledge of leading open source data analysis tools and machine learning libraries
- Experience with tools and frameworks enabling large scale data analysis (e.g., Spark) and advanced programming skills in commonly used languages for analysis (e.g., python, R)
- End-to-end knowledge of the data science problem, including large scale data and data pipeline management
- Proficiency with crafting technical documentation and presentations (white-board, small team, broad audience)
- Entrepreneurial mindset and comfortable to work in a non-hierarchical, large global organization where interaction with senior management is required
- Passion for consistent learning.
- Ability to travel
It’s a Plus if you have:
- Advanced knowledge of AWS, GCP, and/or Azure data science and machine learning services
- Experience applying advanced machine learning to large scale, financial modeling problems
- Master's degree or Ph.D. in a quantitative discipline
We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status or disability status.