Description:
The Senior Artificial Intelligence Scientist will be part of AI product development team to help develop our AI suite of products. This role will also be responsible for building production ready AI products that are optimized for speed, reliability and scale using Pyspark and Python. This role will be a combination of
machine learning, software development and machine learning engineering.
Responsibilities:
Develop production level machine learning software using PySpark and Python- Convert proof-of-concepts and feature requests into production ready code.
- Optimize and transform existing software for speed, reliability, and scale.
- Integrates state-of-the-art machine learning algorithms as well as the development of new
methods.
- Collaborate cross-functionally with data scientists, data engineers, product managers, and other
stakeholders to identify gaps and issues in the AI product suite and propose solutions.
- Participate in all phases of the software development lifecycle including design, coding, unit
testing, and documentation for both new and existing pieces of software.
- Mentors, guides, and indirectly leads less experienced Artificial Intelligence Scientist peers.
- Drives innovation by fostering open, high energy, collaborative environment; lead participation in
innovation summit and expos, recommend relevant training and conferences for employees to
attend, publish paper and patent disclosures.
Qualifications:
Required:
- Master’s or Ph.D in mathematics, statistics, computer science, computer engineering or
related field.
- 5+ years’ experience in data science, machine learning, or software engineering
required.
- Knowledge of statistical programming techniques and languages (e.g., Python, PySpark).
- Working knowledge of common machine learning and deep learning approaches (e.g.
regression, clustering, classification, dimensionality reduction, supervised and
unsupervised techniques, Bayesian reasoning, boosting, random forests, deep learning)
and data analysis packages (e.g. scikit-learn, Spark MLlib).
- Extensive knowledge and experience with time series forecasting.
- Prior experience with machine learning packages (e.g. scikit-learn, TensorFlow, Keras,
PyTorch).