Vice President, Tech Lead & Data Scientist - Strategic Projects Vice President, Tech Lead & Data Scientist -  …

BlackRock
in London, United Kingdom
Permanent, Full time
Be the first to apply
Competitive
BlackRock
in London, United Kingdom
Permanent, Full time
Be the first to apply
Competitive
Vice President, Tech Lead & Data Scientist - Strategic Projects
Description

About this role

Aladdin Client Sales and Solutions
BlackRock's Aladdin Client & Sales Solutions (ACSS) team is responsible for delivering tools, algorithms, and solutions to drive the efficiency of BlackRock's Client Business organizations globally and digitally transform how BlackRock's clients experience the firm. Do you have demonstrated success delivering impact and commercial outcomes? Then, we want you! We are passionate about delivering state of the art technology to our users in a scalable and resilient manner, coupled with a great user experience.

We are a team that recognizes strength comes from diversity and embrace technical expertise and curiosity equally. We are focused on delivering commercial outcomes and are passionate about building outstanding products.

Team and Role background
The mission of the Strategic Projects team is to generate sales and service alpha for clients through data science. The charter for the team is to develop data science foundations in new domains, driving early-stage research and commercial value from high-potential areas for which data science has only begun to be applied. These new areas include Experimentation, Distribution Strategy, Client Segmentation, Client Intelligence, and Sales Efficiency, among others.

The role reports into the London-, New York-, or San Francisco- based Director / Data Science Manager of the Strategic Projects Team, who reports into the Head of Data Science and Insights for ACSS. Potential for formal people management responsibilities leading a permanent team within Strategic Project as and when team gains traction in its explorations of a business domain to the point that there is a need for permanent team focused on that problem space.

Primary responsibilities
Technical Lead
  • Lead technical development of algorithms, systems, and analysis and drive commercial value for a particular project within a business domain of exploration for Strategic Projects. As these data science domains for exploration will shift over time and will depend on the level of traction and commerciality of initial forays, the domain of focus will also shift.
  • For those projects with larger scope and resourcing needs, lead a squad of approximately 2-3 Data Scientists tasked for the exploration. These Data Scientists will sometimes span multiple geographies, when appropriate. Squads will be created and wound down as needed, and the size and members of those teams that the Tech Lead oversees will likely shift from project to project dependent on the nature of the work, team member expertise, and team member interests.
  • Lead regular sprint planning in domain of exploration in order to define what work product will be delivered in the next sprint, broader planning for subsequent sprints, as well as retros of previous sprints.
  • When leading squads, delegate tasks as appropriate to other members of squad, understanding their strengths, interests, and expertise.
  • Partner with product, sales, and service management stakeholders in order to understand business context and needs, update on progress of projects, and provide feedback on feasibility of business priorities.
  • Lead discussions of full Strategic Projects team plus product and business stakeholders in generating new innovative ideas and improvements to existing models within a particular domain of exploration for Strategic Projects. Note that this is independent of whether the project has been resourced with the Tech Lead alone or with a squad.
  • Work focused on a wide variety of Machine Learning / Data Science domains, with potential for work in Natural Language Processing (NLP), predictive modeling, lifetime value modeling, segmentation, forecasting, clustering, causal inference , reinforcement learning, and structural/econometric modeling.

Qualifications
  • BA/BS degree in a quantitative field (Mathematics, Finance, Statistics, Physics, Computer Science, or similar area).
  • Knowledge of scripting languages (Python or R), data querying languages (SQL) and data manipulation, and experience and comfort in data analytics at every level of the stack.


Our benefits

To help you stay energized, engaged and inspired, we offer a wide range of benefits including a strong retirement plan, tuition reimbursement, comprehensive healthcare, support for working parents and Flexible Time Off (FTO) so you can relax, recharge and be there for the people you care about.

About BlackRock

At BlackRock, we are all connected by one mission: to help more and more people experience financial well-being. Our clients, and the people they serve, are saving for retirement, paying for their children's educations, buying homes and starting businesses. Their investments also help to strengthen the global economy: support businesses small and large; finance infrastructure projects that connect and power cities; and facilitate innovations that drive progress.

This mission would not be possible without our smartest investment - the one we make in our employees. It's why we're dedicated to creating an environment where our colleagues feel welcomed, valued and supported with networks, benefits and development opportunities to help them thrive.

For additional information on BlackRock, please visit careers.blackrock.com | www.blackrock.com/corporate | Instagram: @blackrock | Twitter: @blackrock | LinkedIn: www.linkedin.com/company/blackrock

BlackRock is proud to be an Equal Opportunity and Affirmative Action Employer. We evaluate qualified applicants without regard to race, color, national origin, religion, sex, sexual orientation, gender identity, disability, protected veteran status, and other statuses protected by law.

BlackRock will consider for employment qualified applicants with arrest or conviction records in a manner consistent with the requirements of the law, including any applicable fair chance law.
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