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Job Description

Chance to work on financial data for complex problems/challenges. Utilize LLMs/Genai systems and architectures to build and deploy state-of-the-art Genai systems.

Our team collaborates closely with Finance teams to enhance financial planning and strategic decision-making through cutting-edge data-driven solutions. We specialize in a range of initiatives which provides actionable insights into trends and patterns and leveraging Generative AI (Genai) to produce concise, insightful summaries that empower decision-makers. By integrating these innovative approaches, we strive to drive efficiency, accuracy, and impactful outcomes in financial operations.

Key Responsibilities:

Lead the design, development, testing, and global deployment of large-scale time series forecasting models (including Regression models and state of the art time series specific models for example N-BEAST, PatchTST) to support complex retail and e-commerce hierarchies. Introduce causal modeling approaches to conduct impact analysis for future forecast.

Continuously enhance forecasting strategies by incorporating advanced machine learning architectures, including RNNs (sequence modeling), CNNs (temporal feature extraction), and Attention-based mechanisms to improve accuracy, scalability, and robustness in time series forecasting.

Advance causal modeling frameworks to quantify event impacts and integrate causal insights into forward-looking forecasts.

Build and maintain experimentation pipelines (A/B testing, quasi-experiments, multi-armed bandits) for evaluating causal impacts of interventions.

Mentor junior scientists, review research and production code, and ensure reproducibility and scalability in pipelines.

Collaborate with engineering to implement forecasting + optimization systems in production (Airflow, Astronomer, Spark/Ray).

Act as technical lead on multiple projects, balancing research rigor with business delivery.

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to [email protected] learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/.

Skills and Requirements

Strong foundation in Causal Inference, Statistical Analysis, and advanced Machine Learning methods

Hands-on experience with a wide range of ML techniques, with a deep understanding of their advantages and limitations across different scenarios.

Ability to integrate statistical expertise with machine learning methods to maximize the value and interpretability of ML solutions.

Proficiency in Python, SQL, PyTorch, Spark/Ray, and stats/econometrics libraries.

Experience deploying ML systems at scale on cloud platforms (GCP/Azure). Publications and open-source contributions spanning the full spectrum of modern Machine Learning, from Statistical Learning (e.g., Bayesian modeling, causal inference, high-dimensional statistical methods) to Deep Learning (e.g., convolutional and transformer-based architectures) and Reinforcement Learning (e.g., dynamic programming, policy gradient methods).

Exposure to ML observability: drift detection, retraining triggers, and causality-informed monitoring.

Timeseries forecasting - will be working on it but can learn on the job