Quantitative Researcher – Signal Processing

Why Signal Processing?
Financial market data is noisy, non-stationary and constantly changing. Potentially valuable signals are often weak, short-lived and difficult to distinguish from randomness.
This makes many of the problems faced by VNT's Quantitative Research team surprisingly similar to those encountered in radar, telecommunications and signal processing.
The research involves questions such as how to detect weak signals in noisy environments, determine whether observed patterns are genuine or simply statistical noise, model signals whose properties change over time, and establish whether findings are robust enough to remain useful outside the datasets in which they were discovered.
Responsibilities
The role combines independent quantitative research with practical implementation. The successful candidate will be responsible for:
• detecting weak and short-lived signals in noisy environments;
• filtering, denoising and signal preprocessing;
• spectral and time-frequency analysis;
• modelling non-stationary and rapidly changing signals;
• signal detection and statistical estimation;
• applying Machine Learning to complex real-world data;
• adaptive filtering and online learning;
• combining multiple noisy information sources;
• identifying regime changes, anomalies and unusual events;
• formulating hypotheses and designing rigorous experiments;
• critically evaluating whether observed patterns are genuine, robust and exploitable;
• collaborating with other technical teams to integrate successful research into broader systems.
The role provides an opportunity to work with large real-world datasets and test ideas quickly. Promising research can progress from an initial hypothesis to a live production system.

VNT does not expect candidates to follow one specific career path. The key criteria are strong quantitative thinking, genuine research capability, Machine Learning experience and technical depth.
The successful candidate is expected to have:
• a strong foundation in Mathematics, Statistics, Physics, Signal Processing, Electrical Engineering, Computer Science or another highly quantitative field;
• Machine Learning experience, gained through either commercial work or academic/scientific research;
• experience conducting independent research – from formulating hypotheses and selecting methodologies to designing experiments and critically evaluating results;
• strong Python skills and the ability to independently implement and test research ideas;
• experience working with complex, noisy, non-stationary or otherwise challenging real-world data;
• the ability to explain and challenge model choices, assumptions, limitations and results rather than simply applying existing methods or libraries;
• professional English.
Particularly relevant experience may come from DSP, radar or sonar, RF and telecommunications, sensor fusion, detection and estimation, time-series modelling, statistical modelling, applied mathematics, physics, or ML research involving sequential data, robustness or distribution drift.
A PhD or substantial academic research experience in a relevant quantitative field would be a strong advantage but is not required.
Previous experience in quantitative finance, systematic trading or algorithmic trading is considered an advantage but is not a requirement. Candidates coming directly from scientific or academic research are equally relevant if they demonstrate the required technical and analytical depth.
There is also no rigid requirement regarding years of experience. Technical level, research quality and problem-solving ability are more important than seniority alone.
Why join VNT now?
This position comes at an important stage in VNT’s development. Rather than joining an already mature research organisation, the successful candidate will become part of a team that is being actively built and scaled.
VNT plans to add 10+ people over the next 12 months while continuing to strengthen its capabilities across quantitative research and AI. Its longer-term ambition is to develop the team into one of the leading AI and research labs in the region.
This creates an opportunity to:
• join the research function at an early stage of significant growth;
• influence how the team and its research capabilities develop;
• work alongside highly technical researchers from diverse scientific backgrounds;
• explore AI and advanced quantitative methods in real-world research problems;
• work with large datasets and established technological infrastructure;
• see successful research move beyond theory and prototypes into production systems.

VNT offers
• year-end performance-based bonus;
• significant ownership and opportunity to influence the growing research function;
• technically challenging research with direct real-world applications;
• high-end equipment and tooling;
• private health insurance;
• parking and additional benefits;
• flexible working hours;
• regular team activities;
• a collaborative environment focused on technical excellence and strong ideas.
The position is office-based in Vilnius, Lithuania. Occasional work from home may be possible when justified, but there is no regular hybrid or remote model.
VNT is open to international candidates who are willing to relocate to Vilnius and work on-site with the team.