Data Scientist, Systematic Data Science - New York in London

Data Scientist, Systematic Data Science - New York in London

London Full-Time 59400 - 72600 £ / year (est.) No working from home possible
B

At a Glance

  • Tasks: Join our team to analyse financial datasets and support Portfolio Managers with data evaluation.
  • Company: Dynamic financial services firm in New York, fostering innovation and collaboration.
  • Benefits: Competitive salary, health benefits, and opportunities for professional growth.
  • Other info: Collaborative environment with strong focus on data quality and innovative solutions.
  • Why this job: Make a real impact by leveraging data to drive investment strategies and decisions.
  • Qualifications: Master’s or Ph.D. in a quantitative field with 5+ years of relevant experience.

The predicted salary is between 59400 - 72600 £ per year.

We are seeking a Data Scientist to join our team, with strong experience in financial datasets to support Portfolio Managers (PMs) through rigorous data evaluation, research support, and signal discovery. This role is critical in verifying and onboarding new data, ensuring data quality, and providing subject matter expertise to accelerate the investment research process.

Key Responsibilities

  • Assess and validate new data requests from PMs for existence, point-in-time (PIT) availability, and quality.
  • Liaise with PMs to clarify requirements and ensure alignment with business objectives.
  • Evaluate and recommend appropriate data sources for investment analysis.
  • Develop and implement business logic for content QC in partnership with PMs.
  • Design data validation and quality assurance processes, including statistical and rule-based checks.
  • Enhance Python-based data quality checks as part of the onboarding process.
  • Author and maintain comprehensive documentation for data sources, business logic, and QC processes.
  • Provide hands-on support to PMs and analysts in understanding, exploring, and leveraging new datasets.
  • Lead evaluation of new datasets, including exploratory data analysis, feature engineering, and assessment of data suitability for systematic strategies.
  • Collaborate with PMs to design and execute signal research projects, including hypothesis generation, backtesting, and statistical validation.
  • Develop tools and frameworks to accelerate dataset evaluation and signal discovery, enabling PMs to rapidly assess the value of new data sources.
  • Serve as a subject matter expert (SME) for data-related questions, providing recommendations on data sources, structure, and usage.
  • Work closely with Data Engineering to ensure seamless data integration, transformation, and delivery.
  • Provide feedback and requirements to improve data pipelines, infrastructure, and automation.
  • Participate in the design, creation, and maintenance of datasets used firm wide.

Qualifications

  • Master’s or Ph.D. in Computer Science, Mathematics, Physics, Statistics, Financial Engineering, or a related quantitative field.
  • 5+ years of relevant experience as a data scientist/analyst in financial services.
  • Demonstrable experience building data collection, cleansing, and delivery infrastructure.
  • Strong programming skills in Python and SQL; experience with data manipulation libraries (e.g., pandas, NumPy) and Database.
  • Experience with data quality assessment, validation, and documentation.
  • Experience supporting research teams or PMs in dataset evaluation, exploratory data analysis, and signal research.
  • Familiarity with cloud data platforms (AWS S3, Redshift, Snowflake) and workflow orchestration tools (Airflow, Git, Jira).
  • Experience analyzing financial data across asset classes (FX, commodities, fixed income, equity).
  • Experience with vendor data (reference, fundamental, market, alternative) and multi-vendor aggregation.
  • Experience with statistical modeling, machine learning, and building predictive models on financial data.
  • Excellent communication skills, with the ability to translate business requirements into technical solutions.
  • Strong problem-solving skills, intellectual curiosity, and attention to detail.

Data Scientist, Systematic Data Science - New York in London employer: Balyasny Asset Management

Iris is an exceptional employer that fosters a dynamic and collaborative work culture, where creativity and strategic thinking thrive. Located in a vibrant city, we offer our employees ample opportunities for professional growth and development, alongside a comprehensive benefits package that prioritises work-life balance. Join us to be part of a team that not only values your contributions but also encourages you to innovate and make a meaningful impact in the world of brand engagement.

B

Contact Details:

Balyasny Asset Management Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land Data Scientist, Systematic Data Science - New York in London

Get Involved in Data Science Meetups

Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Balyasny Asset Management!

Show Off Your Projects

Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Data Scientist, Systematic Data Science - New York at Balyasny Asset Management.

Leverage Professional Networks

Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Balyasny Asset Management.

Apply Directly through Our Website

When you find a suitable opening like Data Scientist, Systematic Data Science - New York at Balyasny Asset Management, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!

We think you need these skills to ace Data Scientist, Systematic Data Science - New York in London

Data Evaluation
Financial Datasets
Data Quality Assurance
Python
SQL
Data Manipulation Libraries (pandas, NumPy)
Exploratory Data Analysis

Some tips for your application 🫡

Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!

Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!

Craft a Tailored Cover Letter:For a full-time role at Balyasny Asset Management, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.

Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Balyasny Asset Management. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!

How to prepare for a job interview at Balyasny Asset Management

Brush Up on Your Statistics

For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!

Showcase Your Projects

Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!

Get Comfortable with Python and R

Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Balyasny Asset Management!

Prepare for Case Studies

Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.