Lead/Senior Data Scientist

Summary

Join DVT, a global custom software development and data engineering company, as a Lead Data Scientist to assist on a complex project in the renewable energy sector. With remote and hybrid options, you'll work alongside established developers with the latest technologies.

Requirements

• 8+ years of experience working on Data Science projects and ideally having led some of them
• Bachelor’s, Master's or PhD in Computer Science, Statistics, Mathematics, or a related field. A Master’s degree is preferred
• Certifications in data science, machine learning, or related fields are a plus
• Strong knowledge and extensive experience in: Knowledge of tools such SQL, Sci-Kit lean, Pandas, Numpy, jupyter notebook
• Exceptional knowledge of Machine Learning techniques on both the supervised and unsupervised learning fronts
• Exceptional knowledge of time series forecasting
• Track record and demonstratable knowledge of using the above algorithms to solve problems at client or at their employer
• Well versed and good understanding on MLOps best practices and has been a part of a team that has taken models to prod, ideally at scale
• Knowledgeable of MLOps tools like MLFlow, DVC, Airflow or cloud MLOps environments like Sagemaker, Azure ML studio of GCP’s vertex AI (Nice to have if they have practical experience)
• Databricks experience is advantageous

Responsibilities

• Work independently on complex projects
• Design and implement complex predictive models or algorithms to drive business growth and improve customer experiences
• Identify patterns and trends in data to drive insights and recommendations for business decisions
• Design experiments and A/B testing to validate hypotheses and assess impact of data-driven solutions
• Mentor and provide guidance to junior and intermediate data scientists
• Be involved in project management and strategic decision-making
• Be involved in data pipeline architecture design and implementation Collaborate with cross-functional teams to gather requirements, design solutions, and implement models
• Communicate findings and recommendations to non-technical stakeholders in a clear and concise manner