Senior Data Scientist

• 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.
• Lead a DS technical area (e.g. forecasting, anomaly detection, experimentation standards).
• Set/improve shared modeling, metrics, and experimentation practices across squads.
• Coach data scientists and raise consistent quality bars.
• Partner with engineering/platform on reusable feature, evaluation, and productionization patterns.

• 8+ years of Data Science experience.
• B.Sc. in Machine Learning, Statistics, Data Science, Physics, or related field — or equivalent education and industry experience.
• Proficiency in Python and data science libraries (Pandas, NumPy, scikit-learn, and PyTorch or TensorFlow)
• 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.
• 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.
• Track record of multi-team influence - standards, metrics, or practices others adopted.
• Experience coaching scientists, not only personal delivery excellence.
• Ability to prioritize a science agenda for an area across initiatives.

• Past experience: Improved measurement practices across teams and coached other scientists.
• Delivery evidence: Shared metrics or experimentation standards adopted by multiple teams.
• Shared expectation: Leads a technical area and improves shared practices.

• 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).
• M.Sc. in a related field

Our commitment to you!

BMC’s culture is built around its people. We have 6000+ brilliant minds working together across the globe. You won’t be known just by your employee number, but for your true authentic self. BMC lets you be YOU!

If after reading the above, You’re unsure if you meet the qualifications of this role but are deeply excited about BMC and this team, we still encourage you to apply! We want to attract talents from diverse backgrounds and experience to ensure we face the world together with the best ideas!

BMC is committed to equal opportunity employment regardless of race, age, sex, creed, color, religion, citizenship status, sexual orientation, gender, gender expression, gender identity, national origin, disability, marital status, pregnancy, disabled veteran or status as a protected veteran. If you need a reasonable accommodation for any part of the application and hiring process, visit the accommodation request page.