Machine Learning Scientist / Engineer – Financial Intelligence

Position Overview

We are looking for a highly skilled Machine Learning Scientist / Engineer to lead the development and deployment of algorithms that power our financial intelligence applications. In this role, you will bridge the gap between quantitative data science and software engineering by owning the full lifecycle of predictive models—from conceptual hypothesis formulation and prototyping to building scalable production pipelines. The focus will be on applying machine learning and time series forecasting for automating cost variance assessments, cost predictions, scenario and "What-If" analyses, and KPI variance evaluations.

Key Responsibilities

• Design, train, and validate advanced machine learning models and classical statistical approaches tailored for long-term cost forecasting and KPI prediction.
• Utilize sophisticated time series and sequential modeling techniques, such as deep learning architectures, state-space models, and hierarchical forecasting, to capture intricate seasonal trends, macroeconomic influences, and shifts in high-dimensional financial datasets.
• Develop robust simulation engines including Monte Carlo and stress-testing frameworks enabling financial planners to explore interactive "What-If" scenarios modeling the impacts of operational and market changes on cost structures.
• Create automated anomaly detection and diagnostic models to identify root causes of discrepancies between planned, forecasted, and actual financial KPIs.
• Transform prototype codebases into well-structured, scalable production services including containerization, pipeline orchestration, and monitoring systems to track feature and model drift over time.
• Collaborate closely with corporate finance teams to interpret complex statistical results into clear, actionable insights and interactive strategic dashboards.

Expertise and Technical Skills

• Strong foundation in machine learning and statistical methods including supervised and unsupervised learning, probabilistic programming, ensemble methods, and nonlinear regression techniques.
• Extensive experience with time series forecasting methods such as Prophet, ARIMA, DeepAR, Temporal Fusion Transformers, or N-BEATS while managing sparse, noisy, or irregular financial data.
• Proven ability to create simulation and decision science frameworks like sensitivity analysis and Bayesian networks for risk modeling and scenario planning.
• Hands-on expertise in Python programming and its scientific ecosystem, including Pandas, NumPy, Scikit-Learn, and PyTorch or TensorFlow (JAX experience is a plus).
• Strong software engineering skills encompassing version control (Git), unit testing, API design, and familiarity with distributed computing frameworks like Spark or Ray to handle intensive simulations.
• Proficiency in using SQL and cloud-based data warehouses such as Snowflake or BigQuery, alongside MLOps orchestration tools including Docker, MLflow, Airflow, or Kubernetes.

Education and Experience

• Applicants should hold a Master's or Ph.D. degree in Data Science, Computer Science, Statistics, Quantitative Finance, or an equivalent quantitative discipline.
• Minimum of 5 years of professional experience as a data scientist or machine learning engineer, ideally with a track record of applying machine learning techniques to financial, economic, or operational planning datasets.
• Familiarity with corporate finance concepts such as budgeting processes, driver-based planning, cost allocation, and variance analysis is highly advantageous.