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IMC Trading
Generate and validate high-quality alpha signals using statistical methods and machine learning, combine market microstructure and energy fundamentals, and build and maintain reliable, production-ready models.
Requires 2+ years of experience developing quantitative models or trading strategies in financial futures, a strong foundation in probability and statistics, and proficiency in Python for production-quality code.
IMC is continuing to grow its trading initiative in Aarhus, Denmark, to become a major player in the European Power and Gas markets. This expansion is built on a strong foundation of experienced external hires in Trading and internal talent in Technology. With a collaborative and entrepreneurial culture, the team offers a unique opportunity to contribute to shaping and scaling this new business.
The group combines deep expertise with broad trading experience and values innovation, ownership and cross-functional collaboration. As this business continues to grow, there will be increasing opportunities to take on broader responsibilities and contribute meaningfully to its success.
About Us
IMC is a global trading firm powered by a cutting-edge research environment and a world-class technology backbone. Since 1989, we’ve been a stabilizing force in financial markets, providing essential liquidity upon which market participants depend. Across our offices in the US, Europe, Asia Pacific, and India, our talented quant researchers, engineers, traders, and business operations professionals are united by our uniquely collaborative, high-performance culture, and our commitment to giving back. From entering dynamic new markets to embracing disruptive technologies, and from developing an innovative research environment to diversifying our trading strategies, we dare to continuously innovate and collaborate to succeed.
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AI Est. Total Comp
$162,000
Location
Aarhus, Central Denmark Region, Denmark
Work Type
On-site
Seniority
mid
Experience
3-5 years
Category
Data Science
Visa Sponsorship
Unknown
Quality Score
3.8