Senior Data Scientist – Global Services; Hybrid – Newark, NJ

Job Classification:
Technology – Data Analytics & Management

As the Global Asset Management business of Prudential, we’re always looking for ways to improve financial services. We’re passionate about making a meaningful impact – touching the lives of millions and solving financial challenges in an ever-changing world.

We also believe talent is key to achieving our vision and are intentional about building a culture on respect and collaboration. When you join PGIM, you’ll unlock a motivating and impactful career – all while growing your skills and advancing your profession at one of the world’s leading global asset managers!

If you’re not afraid to think differently and challenge the status quo, come and be a part of a dedicated team that’s investing in your future by shaping tomorrow today. At PGIM, You Can!

PGIM Data Science is a newly established team and you will join it as key founding member. As the Data Scientist for PGIM, you play a pivotal role in shaping and executing PGIM’s data-driven strategy, specifically in asset management area. You will partner with other cross‑functional teams, data engineers, financial analysts and business leaders to shape the future of PGIM’s data landscape.

This role closely engages with the development of advanced data science projects and data‑driven insights to significantly elevate business outcomes. You will solve meaningful business problems using advanced machine learning models.

This role is based in our office in Newark, NJ. Our organization follows a hybrid work structure where employees can work remotely and/or from the office, as needed, based on demands of specific tasks or personal work preferences. This position is hybrid and requires your on‑site presence on a recurring weekly basis 3 days per week and can change based on future updates to the company’s in‑office presence policy.

• Drive the development, deployment, and optimization of advanced machine learning models for financial applications, ensuring scalability, accuracy, and robustness.
• Design and implement machine learning models, including regression, classification, clustering, time‑series forecasting, natural language processing (NLP) and reinforcement learning for cross‑divisional asset management mandate.
• Build robust feature engineering pipelines by leveraging financial data sources, transactional datasets, and alternative data.
• Ensure model interpretability and risk mitigation, aligning with model risk management and governance frameworks.
• Conduct model validation, back‑testing, and performance monitoring, implementing adaptive strategies based on market conditions.
• Collaborate with quantitative researchers, financial analysts, and engineering teams to integrate models into real‑time production environments.
• Optimize model efficiency, robustness, and compliance with regulatory guidelines.

• A minimum of a Master’s degree in Statistics, Data Science, Applied Mathematics, Computer Science, or comparable quantitative disciplines. PhD is preferred.
• 1+ years of working experience in advanced machine learning techniques, including:
• Reinforcement Learning (Q‑learning, deep Q‑networks, policy gradient methods)
• Natural Language Processing (text embeddings, topic modeling, entity recognition, transformer‑based models for financial document analysis)
• Hands‑on experience in back‑testing, and performance evaluation and monitoring of the models in asset management industry
• Preferably hands‑on experience in developing LLM, NLP, NLU, NLG, deep learning models, and transformer models, with a focus on developing conversational AI solutions.
• Proficiency in Python, R, SQL, and the corresponding machine learning libraries.
• Preferably strong knowledge of machine learning application in financial industry, but not required.
• Experience in deploying ML models into production, optimizing for efficiency, scalability, and interpretability.
• Excellent documentation, communication and presentation skills, can influence without authority.
• Team‑orient mindset and can‑do attitude.

• Market competitive base salaries, with a yearly bonus potential at every level.
• Medical, dental, vision,…