Lead Data Scientist

BMC Software

3d•$175,800 - $293,000•Remote

About The Position

BMC empowers nearly 80% of the Forbes Global 100 to accelerate business value, faster than humanly possible. Our industry-leading portfolio unlocks human and machine potential to drive business growth, innovation, and sustainable success. BMC does this in a simple and optimized way by connecting people, systems, and data that power the world’s largest organizations so they can seize a competitive advantage. The IZOT product line includes BMC’s Intelligent Z Optimization & Transformation products, which help the world’s largest companies to monitor and manage their mainframe systems. The modernization of mainframe is the beating heart of our product line, and we achieve this goal by developing products that improve the developer experience, the mainframe integration, the speed of application development, the quality of the code and the applications’ security, while reducing operational costs and risks. We acquired several companies along the way, and we continue to grow, innovate, and perfect our solutions on an ongoing basis. Hands-on Machine Learning, statistical modeling, predictive analytics, evaluation, and productionization. Generative AI and agentic systems are owned by AI Engineering; this role stays focused on traditional ML and predictive analytics - with collaboration on GenAI features when modelling expertise adds value. About the Role You will frame business problems as modeling problems, build and validate predictive and analytical models, and partner with product and engineering to put those models to work where they create measurable value. You apply statistical rigor, experimental design, and reproducible ML practice so models are trustworthy for enterprise use.

Requirements

12+ years of Data Science experience
Degree in Machine Learning, Statistics, Data Science, Physics, or related field / equivalent education and industry experience (Master’s preferred for L3+).
Proficiency in Python and data science libraries (Pandas, NumPy, scikit-learn, and PyTorch or TensorFlow) appropriate to level.
Knowledge of statistical analysis, hypothesis testing, experimental design, data mining, and machine learning techniques.
Experience designing performance metrics and evaluation approaches for predictive ML systems (depth scales with level).
SQL and modern data platforms; data exploration and visualization.
Familiarity with cloud platforms such as OpenShift (OCP) and AWS.
Clear communication; ability to work in a multi-tasked, dynamic environment.
Evidence of setting a science agenda beyond one technical specialty.
Major decisions or investments improved by capabilities you established.
Org/BU-level influence with lasting measurement or modeling impact.
Past experience: Set a science agenda across a product area or organization.
Delivery evidence: Major decisions or investments improved by durable measurement capabilities.
Shared expectation: Sets direction and makes consequential technical tradeoffs.

Nice To Haves

Python backend APIs (FastAPI, Flask, or similar).
Exposure to Generative AI / LLMs as a collaborator (not a primary hiring filter).
MLOps practices (MLflow, Kubeflow, experiment tracking, registries, pipelines).
Time-series forecasting, causal inference, or anomaly detection in enterprise systems data.
Agile methodology and Atlassian products (Jira, Confluence)

Responsibilities

Design and develop Machine Learning solutions using statistical analysis, data mining, and classical/modern ML techniques.
Build, train, validate, and iterate predictive models (classification, regression, ranking, forecasting, anomaly detection, clustering) against clear business outcomes.
Perform feature engineering, exploratory analysis, and experiment design to improve model quality and decision usefulness.
Collaborate with domain experts to understand business requirements and formulate data-driven solutions.
Define offline and online metrics, holdout strategies, and monitoring signals for model performance and drift.
Deliver models to enterprise-grade quality: rigorous, validated, reproducible, and ready for mission-critical use.
Support packaging and deployment of models into production with engineering partners; maintain clear handoffs.
Partner with AI Engineering and AI Quality when classical models or predictive signals feed GenAI / agentic workflows.
Explain complex technical concepts and predictive analytics clearly to technical and non-technical audiences.
Set the science agenda for a product area or organization (what to measure, model, and invest in).
Make consequential tradeoffs among modeling approaches, data investments, and partner capacity.
Build durable measurement/modeling capabilities that improve major decisions over time.
Represent DS rigor with senior product, engineering, and business stakeholders.