Senior Associate, Machine Learning Engineer, Investment

1 month ago

Singapore Dbs Bank Ltd. Full time

The Team:

The DBS Investments and Trading Technologies (ITT) Data Team works with stakeholders across all pillars of the Global Financial Markets (GFM) Department to create proprietary algorithms and data science solutions for trading, sales, and operations.

We are seeking a machine learning specialist who thrives in dynamic environments, possesses a strong foundation in data science and a keen interest in leveraging data to drive innovation in the financial markets and banking sector.

Responsibilities:

  1. Utilize extensive financial datasets to drive end-to-end data science solutions for algorithmic pricing, experiment design, process optimization, and customer hyper-personalization.
  2. Work with quants, traders, and dealers to solve complex optimization problems using advanced machine learning and quantitative methods.
  3. Conduct research and literature reviews to assess quantitative algorithms and models, ensuring the adoption of optimal methodologies.
  4. Implement and train AI/ML models, optimizing algorithm and system efficiency through GPU distributed computing and concurrent programming.
  5. Create reusable libraries/ microservices, deploy machine learning ecosystems, and integrate subsystems as necessary.

Required Experience:

  1. Bachelor's degree or higher in a quantitative discipline, such as Mathematics/Statistics, Quantitative Finance, Computer Science, Engineering, or equivalent experience.
  2. Proficient in the data science project life cycle, demonstrated by a proven track record of working with structured, semi-structured, and unstructured data.
  3. Deep understanding and application of various machine learning concepts, their mathematical underpinnings, and trade-offs.
  4. Exceptional programming skills in Python (Or demonstrable ability to pick up new languages) and SQL variants, with knowledge of design patterns, code optimization, and object-oriented design.
  5. Familiarity with software development best practices and tools such as Agile methodologies, Jira, Jenkins, and Git.

Good to have (Two or more):

  1. Familiar with Linux OS, Openshift, Kubernetes for container-based deployment.
  2. Demonstrable expertise in econometrics, statistical modelling, time-series analysis, causal inference, and their applications to pricing and marketing domains.
  3. Hands-on experience in designing and executing digital experimentation and hypothesis testing, including A/B testing, bandit-based experiments, and multivariate analysis.
  4. Experience with language models, RAG concepts, opensource generative AI (GenAI) frameworks and prompt engineering principles.
  5. Experience with machine learning applications in financial markets with a solid understanding of market dynamics, and key drivers.
  6. Experience developing reinforcement learning models and frameworks at scale.
  7. Strong research background with experience conducting literature reviews and prototyping fit-for-purpose custom models.
  8. Experience building scalable machine learning system architectures (microservice, distributed, etc.) and big-data pipelines in production.

Key Skills: python, algorithms & data structures, machine learning, causal inference, SQL, experimentation, A/B Testing, multi-armed bandit, pricing, econometrics, software design, time-series analysis, research, financial markets, behavioral science, quantitative analysis, RAG, large language models.

Internal Description

Description - Internal

The Team:

The DBS Investments and Trading Technologies (ITT) Data Team works with stakeholders across all pillars of the Global Financial Markets (GFM) Department to create proprietary algorithms and data science solutions for trading, sales, and operations.

We are seeking a machine learning specialist who thrives in dynamic environments, possesses a strong foundation in data science and a keen interest in leveraging data to drive innovation in the financial markets and banking sector.

Responsibilities:

  1. Utilize extensive financial datasets to drive end-to-end data science solutions for algorithmic pricing, experiment design, process optimization, and customer hyper-personalization.
  2. Work with quants, traders, and dealers to solve complex optimization problems using advanced machine learning and quantitative methods.
  3. Conduct research and literature reviews to assess quantitative algorithms and models, ensuring the adoption of optimal methodologies.
  4. Implement and train AI/ML models, optimizing algorithm and system efficiency through GPU distributed computing and concurrent programming.
  5. Create reusable libraries/ microservices, deploy machine learning ecosystems, and integrate subsystems as necessary.

Required Experience:

  1. Bachelor's degree or higher in a quantitative discipline, such as Mathematics/Statistics, Quantitative Finance, Computer Science, Engineering, or equivalent experience.
  2. Proficient in the data science project life cycle, demonstrated by a proven track record of working with structured, semi-structured, and unstructured data.
  3. Deep understanding and application of various machine learning concepts, their mathematical underpinnings, and trade-offs.
  4. Exceptional programming skills in Python (Or demonstrable ability to pick up new languages) and SQL variants, with knowledge of design patterns, code optimization, and object-oriented design.
  5. Familiarity with software development best practices and tools such as Agile methodologies, Jira, Jenkins, and Git.

Good to have (Two or more):

  1. Familiar with Linux OS, Openshift, Kubernetes for container-based deployment.
  2. Demonstrable expertise in econometrics, statistical modelling, time-series analysis, causal inference, and their applications to pricing and marketing domains.
  3. Hands-on experience in designing and executing digital experimentation and hypothesis testing, including A/B testing, bandit-based experiments, and multivariate analysis.
  4. Experience with language models, RAG concepts, opensource generative AI (GenAI) frameworks and prompt engineering principles.
  5. Experience with machine learning applications in financial markets with a solid understanding of market dynamics, and key drivers.
  6. Experience developing reinforcement learning models and frameworks at scale.
  7. Strong research background with experience conducting literature reviews and prototyping fit-for-purpose custom models.
  8. Experience building scalable machine learning system architectures (microservice, distributed, etc.) and big-data pipelines in production.

Key Skills: python, algorithms & data structures, machine learning, causal inference, SQL, experimentation, A/B Testing, multi-armed bandit, pricing, econometrics, software design, time-series analysis, research, financial markets, behavioral science, quantitative analysis, RAG, large language models.

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