Senior Data Scientist
Posted on:
April 3, 2025
Job description
The role
We are looking for a Senior Data Scientist to help grow our uniquely positioned model stack.
With a special focus on model and data product development and marketing science, you will take charge of our models in finding ways to improve robustness and streamlining our parameter space. You will also support and lead the scaleable and automated deployment of our models and data products for new customers.
As a fast growing startup, your work will help refine our data platform by refining its engine: our state of the art marketing investment models. Improving our models and data products will directly contribute to our growth. The mission of our data science team is to be the central nervous system of Mutinex.
Responsibilities
Day to day
- Automation of model operations, with a critical focus on quality and customer success.
- Scaling modelling features that are useful for 1 customer to a pan-customer global context.
- Development of novel model and data products.
- R&D POC work using contemporary developments in ML, AI, statistics.
- Excellence in software engineering - observable, resilient, tested at every abstraction layer, secure, scaleable applications that can be deployed with CICD, IaC, complete with versioning of every layer - we are all about excellent production ready data science.
- Writing: creating documentation for both data science and non-technical stakeholders; provide data science lens on insights and outputs from the model, processes, model and data products.
Job requirements
What we are looking for
Required qualifications
- Strong Python - deep knowledge of the standard library, canonical best practices, contemporary trends, knowledge of multiple ML libraries.
- Strong principles in software engineering.
- Strong test-writing discipline.
- SQL proficiency.
- Expertise in Bayesian Statistics.
- Experience working with multivariate time series data.
- Experience implementing statistical inference algorithms (e.g., particle-filters, variational inference).
- Solid understanding of fundamental mathematics for statistics and machine learning - probability theory, linear algebra, and calculus.
- Machine learning fundamentals.
- Bachelors/Masters/PhD in statistics, data science, or comparable quantitative degree - e.g. Engineering, Science.
Bonus qualifications
- Strong cloud computing background, including IaC - GCP and Pulumi preferred, but okay if strong in any cloud and IaC.
- Experience working with a multitude of data stores - relational, OLAP, object, graph, time-series, key value, etc.
- Distributed data processing experience.
- Experience with MLOps.
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