Level

State Street

AQE Research Analyst, Officer

AI in this role

pytorchtensorflowdatabricks
nlp

State Street Investment Management (SSIM) is the asset management business of State Street Corporation, one of the world's leading providers of financial services to institutional investors. As one of the world's largest asset managers, State Street Investment Management manages approximately $6.3 trillion in assets under management (AUM) and serves institutional, intermediary, and individual investors globally through a broad range of index, ETF, active, and quantitative investment strategies.

SE Active (Systematic Equities Active) is a global investment team within State Street Investment Management that develops and manages quantitatively driven active equity strategies across developed and emerging markets. The team combines proprietary research, advanced data science, machine learning, and portfolio construction techniques to deliver innovative investment solutions for institutional and intermediary clients. Strategies span enhanced index, active, defensive, and market-neutral approaches across global equity markets.

SE Active is seeking a Senior Quantitative Researcher who will contribute to the development of next-generation quantitative investment strategies by combining investment insights, alternative datasets, and sophisticated statistical and machine learning techniques.

Job Duties

  • Conceptualize and develop alpha strategies using optimization, machine learning, deep learning, NLP, data science techniques, and economic insights.
  • Back-test and evaluate investment strategies, data vendors, alternative datasets, and predictive signals to drive innovation and enhance alpha generation.
  • Manipulate, engineer, and analyze large structured and unstructured datasets to conduct robust and bias-aware research simulations.
  • Partner with portfolio managers, researchers, and data scientists globally to transition research ideas into scalable investment solutions.
  • Explore and apply emerging AI, machine learning, and advanced analytics techniques to investment research challenges.

Qualifications and Skills

  • Advanced degree in Computer Science, Statistics, Mathematics, Engineering, Physics, or a related quantitative discipline from IITs, NITs, IISc, or other leading institutions.
  • 8 plus years of relevant experience
  • Strong knowledge of probability, statistics, machine learning, pattern recognition, NLP, and time-series analysis.
  • Excellent programming skills in Python, R, MATLAB, or similar scientific computing environments.
  • Experience working with large-scale structured and unstructured datasets.
  • Knowledge of database technologies and data engineering concepts.
  • Strong interest in financial markets and quantitative investing.
  • 2-6 years of industry experience in quantitative research, data science, machine learning, or quantitative development roles.
  • Creative, self-motivated, intellectually curious, and detail-oriented with high standards of integrity and quality.
  • Strong communication skills and ability to collaborate effectively in a global team environment.

Desirable Skills

  • Familiarity with quantitative investing, factor investing, portfolio construction, or investment theory.
  • Experience with distributed computing and large-scale data processing technologies such as Spark, Databricks, Hadoop, Hive, or SparkSQL.
  • Working knowledge of Linux environments.
  • Experience with deep learning frameworks such as TensorFlow or PyTorch.
  • Experience with cloud computing platforms and modern data engineering workflows.
  • Experience with data visualization and analytical tools such as Tableau or Power BI.


About State Street

Across the globe, institutional investors rely on us to help them manage risk, respond to challenges, and drive performance and profitability. We keep our clients at the heart of everything we do, and smart, engaged employees are essential to our continued success.


We are committed to fostering an environment where every employee feels valued and empowered to reach their full potential. As an essential partner in our shared success, you’ll benefit from inclusive development opportunities, flexible work-life support, paid volunteer days, and vibrant employee networks that keep you connected to what matters most. Join us in shaping the future.


As an Equal Opportunity Employer, we consider all qualified applicants for all positions without regard to race, creed, color, religion, national origin, ancestry, ethnicity, age, disability, genetic information, sex, sexual orientation, gender identity or expression, citizenship, marital status, domestic partnership or civil union status, familial status, military and veteran status, and other characteristics protected by applicable law.


Discover more information on jobs at StateStreet.com/careers


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How we rate this

AQE Research Analyst, Officer at State Street rates 83 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

NLPPyTorchTensorFlowDatabricks

Questions you could be asked

  1. What NLP problem have you worked on, and how did you measure whether it actually worked?
  2. Walk me through how you've used PyTorch in your day-to-day work.
  3. What are the limits of TensorFlow that you've run into, and how did you work around them?
  4. What's a project where you used Databricks hands-on?
  5. How would you decide a model or AI system is ready to ship?

Adapt your resume

  • List these exact terms on your resume: NLP, PyTorch, TensorFlow, and Databricks. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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