Sr. Data Scientist
Cadent
- Philadelphia, PA
- Permanent
- Full-time
- Design, train and apply statistics, mathematical models, and machine learning techniques to create scalable solutions to business problems
- Work with machine learning engineers and software developers to deploy models and modeling pipelines to be leveraged inside of business software products
- Contribute iterative improvements to predictive models
- Leverage model governance techniques and frameworks to ensure performance and stability of data science products
- Work with product team to align on product roadmap and goals with business vertical KPIs
- Participate in the Agile / scrum process
- Follow the CRISP-DM process to generate robust documentation associated with iterative work
- Present results and findings to technical audience, product and business stakeholders
- Collaborate effectively with other members of the data science team, engineering research team and broader data services group including but not limited to Machine Learning Engineers, Data Engineers, Analytics Engineers, Software Engineers, Quality Assurance Engineers, and Business Intelligence analysts
- Participate in researching new data, tools, algorithms and tech stack to align with evolving AI & ML industry
- M.S. in Computer Science, Mathematics, Statistics, a related quantitative field, or equivalent practical experience with a focus on machine learning; or the equivalent of B.S with 2-3 years of experience in a similar role
- 1+ years professional experience in a similar role.
- Proven background answering open ended research questions using data, tools and technology
- Ability to write clean, expressive code in Python and/or other tools including PySpark, Scala etc.
- Practical experience building and evaluating machine learning models, preferably with the scikit- learn ecosystem.
- Experience with SQL and reading from relational databases
- Experience using cloud computing ecosystems (e.g., AWS, GCP) is a plus
- Experience with the practical application of computational statistics
- Fundamental understanding of the mathematical workings of standard feature engineering, dimension reduction, machine learning algorithms and model validation & measurement
- Experience with the practical application of computational statistics and complex ML algos including deep learning, GraphDB SVMs, time series forecasting etc. to build and evaluate models
- Demonstrated communication skills including the ability to switch between technical and business contexts
- Familiarity with best practices for software engineering and the use of the scientific Python ecosystem
- Knowledge of LLM technologies, including generative and embedding techniques, modern model architectures, retrieval-augmented generation (RAG), fine tuning / pre-training LLM (including parameter efficient fine-tuning), and evaluation benchmarks
- Media or ad-tech experience a plus