Senior Data Scientist, Specialist

📋 Description

• Collaborate with Associate Data Scientists and Data Scientists to research data sources, perform data cleansing, engage in feature engineering, and develop baseline models.

• Oversee multiple data science model development projects independently.

• Work closely with various cross-functional stakeholders to maintain strong coordination between data science and business teams, including product management.

• Assume complete responsibility for model development from conception to implementation, which includes managing the machine learning pipeline, documentation, model drift, and remodeling strategy.

• Mentor Associate Data Scientists and Data Scientists, enhancing their data science expertise and their interactions with stakeholders.

• Collaborate with the ML team to foresee deployment challenges, prepare for resource requirements, and ensure successful model deployment and retraining.

• Take charge of and delegate critical tickets or issues faced by business stakeholders as directed by the Lead Data Scientist or Director of Data Science.

• Independently navigate and troubleshoot an extensive codebase, focusing on debugging and enhancing code quality.

• Contribute to coding standards and conventions, actively promoting sound coding practices within the data science team.

• Create and maintain comprehensive documentation for ML models, data sources, and methodologies.

• Assist in drafting feasibility documents.

• Actively engage in core team processes such as R&D and suggest process improvements or new methodologies.

• Share knowledge through presentations in periodic internal team sessions and workshops.

• Guide the use of development methodologies (Scrum, Kanban, etc.) and aid in their implementation in group projects by clarifying details like features, tasks, effort estimates, and timing.

⛳️ Requirements

• A Master’s degree in data science, mathematics, computer science, statistics, or a related field with 3+ years of experience, or a Bachelor's degree with 6+ years of experience in data science.

• Strong written, verbal, and interpersonal communication skills, with the ability to effectively collaborate with team members and cross-functional teams.

• Expert-level knowledge of topics such as Classification, Regression, Clustering, Dimensionality Reduction, Association Rule Learning, Bagging (e.g., Random Forests), Boosting (e.g., AdaBoost, Gradient Boosting), Neural Networks and Deep Learning, Model Evaluation and Metrics, Cross-validation, Hyperparameter Tuning, Optimization Algorithms, NLP, and more.

• Practical knowledge of Agile/SCRUM methodologies, with 1-2 years of experience and a solid understanding of relevant epics, features, and tasks, along with associated effort estimations.

• Demonstrated intellectual curiosity to explore the latest advancements in the field, such as LLM/Generative AI models.

• Ability to maintain a macro perspective while anticipating issues related to deployment constraints, data latency, the suitability of specific algorithm implementations, and mathematical details or assumptions.

• A minimum of 1+ years of experience working with a major cloud platform, developing ML models from data exploration to deployment.

• Proficient in handling large datasets within a cloud environment.

• Understanding of how APIs function, along with knowledge of CI/CD (Continuous Integration/Continuous Deployment).

• Familiarity with DevOps and MLOps principles.

• At least 1+ years of experience writing production-grade code in scripting languages such as Bash, Python, and SQL, with the ability to identify areas for improvement, select optimal data structures, optimize code, and create custom scripts for unique scenarios.

• Proven capability to navigate a data lake environment while creating training datasets or feature stores independently.

• Effective skills in creating, deploying, and troubleshooting containers in the context of ML modeling.

• At least 1+ years of experience with version control systems/concepts, such as Git, for tracking changes in machine learning projects.

• Ability to investigate and research ML frameworks, algorithms, datasets, and feature stores, and contribute to solving data science challenges.

• Capacity to provide macro-level thought leadership to junior members, including Data Scientists, regarding best practices, issue identification, causes, and respective resolutions.

🏝️ Benefits

• Health insurance

• Pre-tax spending accounts

• Retirement benefits

• Paid time off

• Short-term and long-term disability

• Employee stock purchase plan

• Life insurance