BlackRock
Gurgaon
Investment Banking & Asset Management
About this role
Job Description
You can work at one of the world’s top FinTech companies. We sell our Aladdin analytics platform to over 100 of the top global financial institutions and corporations, handling many trillion USD of assets.
You can help conduct research to build quantitative financial models and portfolio analytics that help managing most of the money of the world’s largest asset manager.
You can bring all yourself to the job. From the top of the firm down we embrace the values, identities and ideas brought by our employees.
We are looking for curious people with a strong background in quantitative research, data science and machine learning, have awesome problem-solving skills, insatiable appetite for learning and innovating, adding to BlackRock’s vibrant research culture.
If any of this excites you, we are looking to expand our team. We currently have open applied researcher roles in the portfolio modeling and analytics team within the Financial Modeling Group (FMG) in Gurgaon.
We are building next generation liquidity, security similarity and pricing models leveraging our expertise in quantitative research, data science and machine learning. The models we build use innovative machine learning approaches, have real practical value and are used by traders and portfolio managers alike.
The FMG is a diverse and global team with a keen interest and expertise in all things related to technology and financial analytics. The group is responsible for the research and development of quantitative financial models and tools across many different areas – single-security pricing, prepayment models, risk, return attribution, liquidity, optimization and portfolio construction, scenario analysis and simulations, etc. – and covering all asset classes. The group is also responsible for the technology platform that delivers those models to our internal partners and external clients, and their integration with Aladdin. FMG conducts leading research on the areas above, delivering state-of-the-art models. FMG publishes applied scientific research frequently, and our members present regularly at leading industry conferences. FMG engages constantly with the sales team in client visits and meetings.
Background and Responsibilities
The role is for an applied researcher in the FMG Portfolio Modeling Analytics team to work closely with other researchers to support Ishares Portfolio Managers. We build cutting edge analytics using a wide range of methodologies (Random Forrest, GBM, ….) and a broad array of technologies (Python, Scala, Spark/Hadoop, GCP, Azure). This role is a great opportunity to work closely with the ishares team, spanning areas such as:
- Evaluate trading data, including pre-processing, feature engineering, variable selection, dimensionality reduction etc.
- Design, develop, evaluate, and monitor predictive models that help business drive their decisions.
- Leverage machine learning to extract insights from data and work with investment managers to put those into action.
- Algorithmic quality control process to detect, escalate and fix data anomalies.
- Visualization tools to enable traders & risk managers evaluate performance and risk measures.
Qualifications:
- B.Tech / B.E. / M.Sc. degree in a quantitative discipline (Mathematics, Physics, Computer Science, Finance or similar area). M.Tech. / PhD is a plus.
- Knowledgeable about data mining, data analytics, data modeling, data access, and data storage techniques
- Passionate about creating clean code and have a strong foundation in coding and building data pipelines
- Ability and willingness to learn fast, multi-task, self-motivate and pick up new things easily
- Ability to work independently and efficiently in a fast-paced and team-oriented environment.
- Previous experience with Cloud infrastructure (GCP, AWS, Azure) and working for top Tech companies is a big plus.
- Confident in building models to solve problems including time series forecasting, clustering problems and hands on experience with a range of statistical and machine learning approaches.
- Strong background in Mathematics: statistics, probability, linear algebra is a big plus.
- Previous experience or knowledge in market liquidity is not required but a big plus.
- Knowledge of fixed income and credit instruments and markets a plus.
For recent graduates or professionals with no prior financial industry experience, this position is a unique opportunity to gain in-depth knowledge of the asset management process in a world-class organization.
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.
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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