Senior Data Scientist

Key Responsibilities

·         Design and train predictive models, including deep learning models, to optimize key business outcomes and address complex challenges.

·         Engineer impactful features and build robust, automated data pipelines to support model training and inference.

·         Rigorously analyze model performance and systematically fine-tune models to maximize business metrics.

·         Build and deploy scalable, low-latency, production-grade applications to serve models in a production cloud environment.

·         Use a production-first mindset to consult on project feasibility and help shape technical strategy, balancing performance, time, and cost.

·         Design and validate new processes, products, and advanced ML techniques through experimentation and testing.

·         Analyze large structured and unstructured datasets to identify insights and propose strategies to address complex technical challenges.

·         Communicate complex findings and strategic recommendations to technical and non-technical audiences concisely and effectively.

·         Proactively engage with team members to accomplish individual and group goals, providing technical mentorship to other data scientists.

·         Continuously seek new opportunities to develop and apply new skills towards the improvement of data science processes and technical methodologies.

·         Establish comprehensive monitoring for model performance and data drift, using insights to drive continuous improvement.

·         Document complex model architectures, deployment processes, and data pipelines to ensure maintainability.

Critical Qualifications

·         Bachelor’s degree in a discipline such as Computer Science, Data Science, Engineering, Applied Math, or a related field.

·         5+ years of relevant work experience in a data science or machine learning role with demonstrated history of project ownership and delivery.

·         Proven experience in designing, training, and deploying machine learning models into production environments with measurable impact.

·         Extensive experience working in a cloud environment (GCP is preferred, but cloud skills are transferable).

·         Expert proficiency in SQL and Python, with strong ability to write clean, efficient, and reusable code for both model training and application development.

·         Expertise in a wide range of machine learning algorithms, statistical methods, and deep learning frameworks (e.g. scikit-learn, Keras, TensorFlow, PyTorch).

·         Experience building Python-based applications to serve model predictions for online inference.

·         Experience applying Natural Language Processing techniques to build solutions using unstructured data.

·         Expertise with using Git in a collaborative team environment.