Volatility Data Engineer - Systematic Data Platform

Millennium Management LLC
London
2 months ago
Applications closed

Volatility Data Engineer - Systematic Data Platform

The systematic data group is looking for a Data Engineer/Scientist to join our growing team. The team consists of content specialists, data scientists, analysts, and engineers who are responsible for discovering, maintaining, and analyzing sources of alpha for our portfolio managers. This is an opportunity for individuals who are passionate about quantitative investing. The role builds on individual’s knowledge and skills in four key areas of quantitative investing: data, statistics, technology, and financial markets.

Given the growing success of our Systematic Volatility business, the ideal candidate will leverage options, reference, exchange, and TIQ-level data sets to arm Portfolio Managers with the necessary information to make better, real-time investment decisions.

Responsibilities

  1. Build systematic option platform related to referential data, pricing data, data analysis, and data research.
  2. Work closely with data scientists/analysts for the end-to-end life cycle of data.
  3. Support quantitative researchers/portfolio managers on data related used for signal generation, back testing, and trading.

Qualifications

  1. Strong technical skills with experience in production coding in Linux.
  2. At least 2 years of experience in finance, finance technology, or comparable industry – buyside experience preferred.
  3. Master's degree or higher in fields such as quantitative finance, engineering, computer science, or equivalent.
  4. Proficient in computer science fundamentals and object-oriented programming using Python, C++, Java, or equivalent.
  5. Self-driven and eager to learn, able to pick up things quickly.
  6. Experience with databases/SQL and basic understanding of financial products.
  7. Hard-working, intellectually curious, and team-oriented.
  8. Strong communication skills.
  9. Experience with option security master, option data, option trading, or option vol is a strong plus.
  10. Experience with technologies like KDB, Apache Iceberg, and Lake Formation will be a meaningful differentiator.

#J-18808-Ljbffr

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Careers

Tips for Staying Inspired: How Data Science Pros Fuel Creativity and Innovation

Data science sits at the dynamic intersection of statistics, computer science, and domain expertise, driving powerful innovations in industries ranging from healthcare to finance, and from retail to robotics. Yet, the daily reality for many data scientists can be a far cry from starry-eyed talk of AI and machine learning transformations. Instead, it often involves endless data wrangling, model tuning, and scrutiny over metrics. Maintaining a sense of creativity in this environment can be an uphill battle. So, how do successful data scientists continue to dream big and innovate, even when dealing with the nitty-gritty of data pipelines, debugging code, or explaining results to stakeholders? Below, we outline ten practical strategies to help data analysts, machine learning engineers, and research scientists stay inspired and push their ideas further. Whether you’re just starting out or looking to reinvigorate a long-standing career, these pointers can help you find fresh sparks of motivation.

Careers

Top 10 Data Science Career Myths Debunked: Key Facts for Aspiring Professionals

Data science has become one of the most sought-after fields in the tech world, promising attractive salaries, cutting-edge projects, and the opportunity to shape decision-making in virtually every industry. From e-commerce recommendation engines to AI-powered medical diagnostics, data scientists are the force behind innovations that drive productivity and improve people’s lives. Yet, despite the demand and glamour often associated with this discipline, data science is also shrouded in misconceptions. Some believe you need a PhD in mathematics or statistics; others assume data science is exclusively about machine learning or coding. At DataScience-Jobs.co.uk, we’ve encountered a wide array of myths that can discourage talented individuals or mislead those exploring a data science career. This article aims to bust the top 10 data science career myths—providing clarity on what data scientists actually do and illuminating the true diversity and inclusiveness of this exciting field. Whether you’re a recent graduate, a professional looking to pivot, or simply curious about data science, read on to discover the reality behind the myths.

Careers

Global vs. Local: Comparing the UK Data Science Job Market to International Landscapes

How to evaluate salaries, opportunities, and work culture in data science across the UK, the US, Europe, and Asia Data science has proven to be more than a passing trend; it is now a foundational pillar of modern decision-making in virtually every industry—from healthcare and finance to retail and entertainment. As the volume of data grows exponentially, organisations urgently need professionals who can transform raw information into actionable insights. This high demand has sparked a wave of new opportunities for data scientists worldwide. In this article, we’ll compare the UK data science job market to those in the United States, Europe, and Asia. We’ll explore hiring trends, salary benchmarks, and cultural nuances to help you decide whether to focus your career locally or consider opportunities overseas or in fully remote roles. Whether you’re a fresh graduate looking for your first data science position, an experienced data professional pivoting from analytics, or a software engineer eager to break into machine learning, understanding the global data science landscape can be a game-changer. By the end of this overview, you’ll be better equipped to navigate the expanding world of data science—knowing which skills and certifications matter most, how salaries differ between regions, and what to expect from distinct work cultures. Let’s dive in.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.