Senior Data Scientist
New
S
SupermetricsSaaS
The UKFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years
- Required Skills
- AWSPythonSQLGCPMachine LearningNumpyPyTorchPandasTensorflowscikit-learnMLOpsLangChain
Requirements
- Advanced degree in a STEM-related field (Mathematics, Statistics, Computer Science, Engineering) or equivalent work experience.
- 5+ years of experience in Data Science or ML and AI with real-world data within a product development and/or research setting.
- Expertise in statistical analysis, ML modeling, experimentation, and optimization.
- Hands-on experience working with LLMs and AI agents, including fine-tuning, retrieval-augmented generation (RAG), and deploying models.
- A proactive mindset, continual pursuit of new ideas and data sources, and ability to systematically tackle ambiguous problems.
- Proficiency in Python and SQL.
- Experience using libraries such as Pandas, NumPy, Scikit-learn.
- Experience using deep learning frameworks like TensorFlow or PyTorch.
- Experience working with Agent frameworks (e.g., google adk, LangChain) and AI agent evaluation.
- Expert-level experience building and deploying data science products using cloud platforms (e.g., AWS, GCP).
- Expert-level experience building and deploying data science products using MLOps tools.
- Strong project management experience.
- Excellent written and verbal communication skills.
- Domain knowledge of large-scale, data-driven SaaS applications.
Responsibilities
- Architect and implement robust, data-driven solutions that solve high-stakes business problems for customers.
- Build and embed insights directly into the Supermetrics product suite.
- Pioneer new solutions and lead with a relentless "product-first" focus.
- Leverage Supermetrics’ unique dataset to build market-differentiated products.
- Identify opportunities to use advanced data science techniques leveraging MarTech data.
- Develop and deploy AI/ML solutions, creating new products and value for customers.
- Transform data science prototypes into scalable, production-ready, data-driven products.
- Embed AI-powered features and automation tools in data science applications.
- Communicate the impact and outcomes of data science initiatives to stakeholders.
- Stay current with AI/ML advancements and continually seek new ideas.
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