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Senior Associate / Assistant Vice President, Investment Data Science (9824)

Employer
Temasek International Pte Ltd
Location
Singapore, Singapore
Salary
Competitive
Closing date
Nov 7, 2023

View more

Job Function
Banking
Industry Sector
Finance - General
Employment Type
Full Time
Education
Bachelors
Company : Temasek International Pte. Ltd.
Dept : Investment Data Science
Position : Senior Associate / Assistant Vice President, Investment Data Science
Reporting : Vice President, Investment Data Science

Overview of the Team and Department

Our Portfolio Strategy & Risk Group (PSRG) actively guides the firm's investment activities and drives sustainable returns, while protecting the portfolio and the institution. The group actively explores overlay and alternative portfolio strategies to enhance our portfolio returns.

Temasek's Investment Data Science (IDS) team works closely with our colleagues to generate investment insights by taking advantage of cloud computing resources, alternative datasets, advanced data science/machine learning techniques, and decades of collective investing experience.

The IDS team is a part of PSRG and supports its activities through :
  • Investment due diligence and monitoring of investments proposed or made by the Investment Group (IG). To research and build data science / statistical models to help IG make better investment decisions.
  • Quantitative and Systematic Investment Strategies to complement the firm's fundamental bottom-up investing. To research and refine quantitative signals (from alternative data as well as more traditional quant/style/accounting metrics) to enhance returns on a fundamentals-driven portfolio.
Roles & Responsibilities

This role will join the team in all areas of work but would be focused on researching and developing quantitative signals for input into the firm's overall Systematic Investment Strategies.

Key responsibilities include:
  • Applying machine learning and/or statistical techniques to derive trading signals from fundamental, financial, and alternative data
  • Collaborating with our team of Investment Data Scientists and Engineers on predictive analytics, cross-sectional and time series analysis, and natural language processing related projects
  • Collaborating with our PSRG colleagues to validate and implement trading signals
The ideal candidate would have the ability to initiate new signal research, derive signals from structured and unstructured data, and evaluate signals using a range of metrics from both a single-trade and portfolio perspective. A disciplined quantitative mindset - e.g. distinguishing correlation and causation, in-sample vs. out-of-sample fit or recognizing statistical significance - will be important for the generation of trade signals and investment strategies that can be executed in real-world markets with transaction costs and potential execution slippages.

Requirements

Minimum Requirement
  1. Masters or PhD in a quantitative field (e.g. statistics, mathematics or economics with a demonstrated application to Finance)
  2. 3-5 years of prior buy-side work experience researching and developing signals using a range of datasets
  3. Understanding of and appreciation for basic micro and macroeconomic dynamics
  4. Understanding of how models can be built to explain or predict how businesses work or market move
  5. Knowledge of how to interact with databases (e.g. PostgreSQL or Snowflake)
  6. Programming skills in Python or R notebooks
  7. Experience applying statistical tests to datasets, and working knowledge of their underlying theory
  8. Empirical, detail-oriented mindset
  9. Strong oral and written communication and interpersonal skills.
  10. Strong problem-solving skills
  11. Intellectual curiosity and passion
  12. Proven critical thinking skills


Preferred Requirement
  1. Experience presenting signals or trading ideas to investment decision making managers or committees
  2. Experience with the ecosystem of common data science packages: Numpy / Scipy / Scikit-learn, Pandas, R's Tidyverse, xts, caret, etc.
  3. Working knowledge of visualisation tools such as Tableau or Qlik

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