Senior Data Scientist I

Summary

We are seeking a highly motivated and experienced Senior Data Scientist I to join our team. The ideal candidate will have a strong understanding of data science principles, a proven track record of deploying robust machine learning solutions, and a passion for leveraging data to drive decision-making. The team primarily focuses on use cases for contact center optimization, building innovative data-driven solutions and applications for seamless business integration. This role offers the opportunity to drive technical direction in an innovative and collaborative team environment working on a high-impact data product.

Key Responsibilities

• Lead the end-to-end lifecycle of complex data science projects, from ideation to production monitoring.
• Design and train predictive models, including deep learning models, to optimize key business outcomes and address 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.
• Actively partner with Product and Engineering teams to influence product design and align on solution feasibility.
• 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 groups goals, providing technical leadership and 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, machine learning, or analytics role with demonstrated history of project ownership and delivery
• Proven experience in designing, building, 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.
• Proven ability to perform statistical analysis to validate model performance and business impact.
• Strong communication and presentation skills, with the ability to convey complex technical concepts to both technical and non-technical audiences.
• Demonstrated experience leading technical projects and mentoring team members.

Preferred Qualifications

• Master’s or Ph.D. in a discipline such as Computer Science, Data Science, Engineering, Applied Math, or a related field.
• Professional certification in cloud-based data platforms and services (e.g. GCP Professional Machine Learning Engineer).
• In-depth knowledge of ML Ops principles and familiarity with associated methods/tools.
• Demonstrated experience building ML models for matching, ranking, or recommendation systems.