Volatility Data Engineer - Systematic Data Platform

Millennium Management LLC
London
1 month ago
Applications closed

Related Jobs

View all jobs

Junior Quantitative Analyst

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.

Job-Hunting During Economic Uncertainty: Data Science Edition

Data science has become essential for modern businesses, enabling data-driven decisions that enhance efficiency, profitability, and strategic foresight. From predictive analytics in finance to recommendation engines in retail, data scientists sit at the crossroads of statistics, programming, and domain expertise, building models that translate raw information into tangible insights. Yet, when broader economic forces create uncertainty—through market downturns, shifting investor priorities, or internal budget constraints—data science roles can experience increased scrutiny, competition, and extended hiring cycles. Despite these pressures, data-driven approaches remain crucial to organizations looking to weather challenges and find opportunities in volatile environments. Whether you’re refining advanced machine learning techniques, fine-tuning data pipelines, or collaborating with business stakeholders on dashboards, your skill set is often still in demand. The key is adapting your job search strategy and personal branding to cut through the noise when fewer roles may be available. This article explores: Why economic headwinds affect data science hiring. Actionable strategies to stand out in a tighter job market. Ways to emphasize your technical and soft skills effectively. Techniques to maintain focus and resilience despite potential setbacks. How www.datascience-jobs.co.uk can help you secure the ideal data science position. By combining strategic thinking, polished communications, and adaptability, you can land a fulfilling data science role that leverages your expertise—even if the market feels more demanding.

How to Achieve Work-Life Balance in Data Science Jobs: Realistic Strategies and Mental Health Tips

Data science has quickly emerged as one of the most influential fields in the modern workplace, shaping how businesses make critical decisions and expanding our understanding of everything from consumer behaviour to public health trends. As the global demand for data insights continues to rise, companies of all sizes are seeking professionals who can effectively harness analytics and machine learning to drive innovation. Yet, this promise of limitless opportunity comes with significant challenges. Data scientists often grapple with large volumes of complex data, tight project deadlines, cross-functional collaboration, and constant upskilling. Amid the excitement of discovering actionable insights and developing high-performing models, many wonder if it’s truly possible to maintain a healthy work-life balance. With the field evolving at breakneck speed, the intensity of these roles can leave even the most passionate data professionals drained and questioning how to protect their personal well-being. This article delves into the reality of high-intensity data science jobs, offering practical guidance on setting realistic expectations and preserving mental health. We’ll look at common pitfalls like burnout and perpetual “always learning” pressure, while also highlighting proven strategies for boundary-setting and effective time management. Whether you’re a seasoned data architect, an aspiring machine learning engineer, or somewhere in between, you’ll find actionable tips to help you manage your career trajectory without sacrificing your personal life.

From Research Labs to Real-World Insights: How Academic Researchers Can Thrive in Data Science

Over the past decade, the data science revolution has swept across industries—retailers predicting shopping habits, hospitals personalising patient treatments, and finance companies detecting fraud in near-real time. This surge in data-driven decision-making has created a pressing need for specialists capable of extracting meaningful insights from complex datasets. As a result, PhD holders and academic researchers are uniquely positioned to bring high-calibre analytical skills to the commercial realm. But transitioning from academia to data science can seem daunting: Where do you begin? How do you communicate your research background in industry-friendly terms? What new tools should you learn? This comprehensive guide, explores the paths, challenges, and strategies for academics making the leap into data science. You’ll discover how to leverage your scholarly expertise, adopt a commercial mindset, and build a career that transforms raw data into actionable insights—fostering innovations across every sector of the economy.