Senior Data Scientist

Define and ensure the adoption of best practices in decision analytics. Assist leadership in setting strategies for data science platforms, ensuring alignment with business objectives, and increasing the effectiveness of the data science team. Engage with stakeholders to align projects with business goals and expectations, translating findings into actionable insights and recommendations. Capture business requirements and translate them into actionable data science solutions that address complex business challenges. Oversee data science projects, ensuring they stay within scope, time, and cost while producing non-technical reports detailing successes and shortcomings. Design, implement, and assess advanced statistical models and machine learning algorithms to solve business problems and generate actionable outcomes. Develop and maintain analytics infrastructure by deploying, managing, and monitoring machine learning models in production reliably and efficiently. Ensure deployed models continue to perform over time, identifying points of retirement and transitioning models, as necessary. Collaborate with data engineers, product managers, ML Ops, and stakeholders to create repeatable machine learning pipelines and enhance decision-making capabilities. Engage in continuous learning to apply new methods, technologies, and tools to enhance data science outcomes. Regularly review junior team members' performance, provide actionable feedback, and offer training to enhance their technical and business skills.

Master's degree in Computer Science, Data Science, Business Analytics, or a related field and three (3) years of experience in data science.

Must have 3 years of experience in all the following:

• Deploying, building, and training machine learning models;
• Python programming;
• Implementing algorithms for machine learning;
• SQL for data querying and manipulation; and
• Optimization techniques such as linear and integer programming, gradient-based methods, genetic algorithms, or simulated annealing and their application in solving complex problems such as supply-chain design, network optimization or capacity planning.

Must have 1 year of experience in the following:

• AWS tools for cloud-based model training and deployment;
• Docker containers, Kubernetes orchestration, or similar technologies;
• TensorFlow, Scikit-learn, Pydantic AI, or similar platforms for developing and training machine learning models;
• MLOps practices, including model deployment, monitoring, and maintenance; and
• Natural gas trading.