Data Scientist

Description

• Envision the bigger picture to build scalable, maintainable analytics systems, leveraging ML (machine learning) platforms, tools, and frameworks to implement predictive analytics for pipeline integrity, asset health, and risk forecasting for very large data sets (e.g. Databricks, Spark, Azure ML, TensorFlow, Hadoop.)
• Act as a support agent to the engineering consulting and sales teams by engaging with customers as a data science subject matter expert, identifying client challenges & opportunities (product and services) for integrity management.
• Serve as a liaison between clients and software developers to ensure alignment between data insights and product functionality. Configure and support the productization deployment of analytical components into Dynamic Risk’s software platform. Support the product development team in leveraging data science methodologies and algorithms for master data management.
• Develop and lead internal and customer-facing demonstrations, collecting feedback to iterate with the Development team on ML based product design.
• Lead technical documentation efforts including data mapping, analytics configurations, and user documentation. Deliver client training sessions and provide post-deployment support as needed.
• Conduct data science problem discovery workshops with customers and translate them into machine learning solutions using traditional, generative, and agentic AI approaches. Apply data mining, data modeling, natural language processing, and statistical techniques to extract insights. Building cloud-based predictive and prescriptive insights to customer problems through data mining/analysis, ML/AI tools and algorithms. Translate the complex analytics into simple, actionable visual insights using tools such as Tableau, PowerBI, and Jupyter. Communicate results clearly to both technical and non-technical stakeholders.
• Work closely with the Software Product Manager to develop and prioritize customer specific data science solutions for productization based on market and customer need.
• Hands-on with data manipulation tools, frameworks, and platforms (e.g., BigQuery, Snowflake, Databricks, Spark, Hadoop, SQL, panadas, NumPy, PowerBI, Tableau, Azure ML)
• Participate in industry events and conferences; contribute to the development of white papers and other thought leadership materials.

Requirements

• Experience with cloud and big data tools (e.g., TensorFlow, Azure ML)
• Bachelor’s degree in Data Science, Statistics, Mathematics, Computer Science, or a related field is required.
• Minimum of 5 years of practical experience in data analytics, machine learning, or a similar field.
• Proficient in Python and SQL (T-SQL, pgSQL, PL/pgSQL).
• Experience with cloud platforms such as Azure, AWS, or GCP.
• Proven experience with Python
• Experience in pipeline or pipeline integrity domain is a strong asset.
• Strong presentation and storytelling skills with a customer-focused mindset.
• C# experience is an asset.
• Familiarity with data infrastructures including data warehouses, datamarts, and data lakes.
• Strong knowledge and expertise of statistical analysis, algorithms, and ML techniques (e.g., classification?, regression, boosting, forests, text mining) including newer technologies such as Generative and Agentic AI.
• Familiarity with DevOps and project management tools such as Azure DevOps and Jira

Benefits

Training + Development

Interview process

Visa Sponsorship

Security clearance