Data Scientist, Applied AI

3 weeks ago

Singapore BALYASNY ASSET MANAGEMENT (SINGAPORE) PTE. LTD. Full time
Roles & Responsibilities

Balyasny Asset Management (BAM) is a global, multi-strategy investment Firm with over $21 billion in assets under management. We are a diversified business, with global breadth and depth. Our Firm has a clear mission: To consistently deliver uncorrelated returns in all market environments. Today, BAM employs more than 160 portfolio managers and 1,200 investment professionals across 19 offices in the U.S., Europe, the Middle East, and Asia. We are active across six investing strategies: Equities Long/Short, Equities Arbitrage, Macro, Commodities, Systematic, and Growth Equity. We also have a dedicated private investment team, BAM Elevate, and a standalone equities unit, Corbets Capital.


As a Data Scientist on our Applied AI Team at BAM, you will:

  • Work collaboratively with senior AI engineers and quantitative researchers to research and build AI systems
  • Devise and develop AI solutions that leverage cloud-based data and distributed computing technologies
  • Assist with managing the AI software development cycle including setting up and maintaining CI/CD pipelines
  • Provide support for the system and be proactive in resolving issues in a timely manner
  • Work as part of a globally distributed yet close-knit team. Be an active participant in feature brainstorming, technical design sessions, code reviews, and general interaction
  • Create software that is well-commented, well-understood, well-tested, and well-documented: Quality above all

To be considered a good cultural fit, you must be:

  • An ambitious self-starter
  • Driven, self-motivated, and creative
  • Hungry to learn
  • Driven towards success
  • A very strong and efficient communicator
  • Able to multi-task and excel in a fast-paced trading environment
  • A problem solver; able to develop quick and sound solutions to complex problems
  • Humble, self-aware, and willing to risk pushing back to make the team and others better

To be considered a good technical fit, you must have:

  • Postgraduate degree in a quantitative discipline such as Computer Science, Data Science, Statistics or Mathematics
  • 3+ years of professional experience developing high quality Data solutions and products as an individual contributor
  • A passion for data and AI, and experience harnessing that passion to educate others
  • Strong knowledge of AI engineering best practices, object-oriented concepts, and the ins and outs of data-focused development
  • Expert in Python (including its data wrangling and machine learning libraries)
  • Solid understanding and demonstrable experience applying natural language processing, large language models and machine learning to solve real business problems
  • Familiarity with a wide variety of LLM architectures, including the GPT, Llama, Mixtral and Claude models
  • Experience customizing LLMs for specific applications using fine-tuning techniques, (e.g. PEFT), RAG and prompt engineering is big plus
  • Experience evaluating and benchmarking LLMs using frameworks such as Arthur Bench is big plus
  • Experience with the entire AI software development cycle from design to deployment e.g. using frameworks such as LangChain is a big plus
  • Experience with basic DevOps techniques, including CI/CD and infrastructure-as-code
  • Experience working in at least one cloud environment. Familiarity with AWS is a big plus

Bonus points for:

  • Research publications in leading journals and presentations at internationally renowned conferences
  • Teaching experience and the development of training material for AI use-cases
  • Experience developing models for named-entity recognition
  • Financial industry experience, though this is not required for the role

Don’t have all the skills listed above? Have extra skills you think are important that we haven’t thought of? Please, let us know by applying and telling us a bit more about yourself and why you think you’re qualified


Tell employers what skills you have

Machine Learning
Product Innovation
Asset Management
Data Wrangling
Pipelines
Natural Language Processing
NLTK
Mathematics
Python
Teaching Experience
Statistics
Arbitrage
Data Science
Django
Equities

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