Senior Data Scientist
About The Position
BMC is seeking a Senior Data Scientist to help build its next-generation Agentic AI platform from 0–1. This hands-on role is for an experienced data scientist who applies machine learning and statistical techniques to complex real-world problems and carries ideas from experimentation through to measurable customer impact. The Senior Data Scientist will work alongside the VP of Engineering, VP of AI, and product leadership to define how data science and machine learning are used across the platform. The role focuses on exploring complex datasets, developing and evaluating models, designing experiments, and translating ambiguous business problems into robust data science solutions. Success is measured by the quality of decisions and product outcomes enabled by the work, rather than model complexity or novelty.
Requirements
- At least 5 years of professional experience in Data Science, Machine Learning, or a closely related field, with experience operating at senior or principal level.
- Expert-level Python and strong experience with the modern Python data science ecosystem.
- Hands-on experience with common machine learning and data science frameworks such as scikit-learn, PyTorch, TensorFlow, XGBoost, or equivalent.
- Strong understanding of model performance, bias, explainability, drift, and the limitations of machine learning systems.
- Experience applying data science within GenAI, LLM, or Agentic AI products.
- Hands-on experience building or integrating LLM-powered applications.
- Experience with LLM APIs and frameworks such as OpenAI, Anthropic, LangGraph, LangChain, or LlamaIndex.
- Strong hands-on experience designing and implementing evaluation frameworks for LLM and Agentic AI systems, including measuring quality, reliability, and performance.
- Experience partnering closely with software engineers, product managers, and other technical stakeholders.
- Ability to explain complex modelling decisions and findings to technical and non-technical audiences.
- Comfort operating in ambiguity and making pragmatic decisions with incomplete information.
Nice To Haves
- Experience with fine-tuning techniques including LoRA, QLoRA, or preference optimisation.
- Experience deploying ML or AI workloads through AWS SageMaker, Azure ML, Vertex AI, Kubernetes, or equivalent.
- Understanding of production AI considerations including inference, observability, latency, cost, guardrails, and scalability.
Responsibilities
- Lead the design and development of machine learning and data science solutions for complex product and customer problems.
- Take data science initiatives from problem definition and experimentation through validation and production adoption.
- Build machine learning models where appropriate.
- Apply statistical analysis and experimentation to measure product impact.
- Explore large and complex datasets to identify signals that can improve product capabilities.
- Develop robust approaches to model validation and performance evaluation.
- Define meaningful metrics and evaluation frameworks for traditional ML and GenAI-powered capabilities.
- Work with product and engineering teams to translate customer and business problems into data science solutions.
- Use Python and modern data science tooling to build reproducible modelling and experimentation workflows.
- Evaluate emerging techniques across machine learning, LLMs, and GenAI to determine where they provide genuine product value.
- Communicate complex analytical findings clearly to engineering, product, and senior leadership.
- Operate with a high degree of ownership, identify opportunities, and move forward without waiting for perfectly defined requirements.
Benefits
- Variable plan and country-specific benefits may be part of the employee compensation package.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
