Data Scientist

Role: DATA SCIENTIST

The Role

Develops and program methods, automated processes, and systems to cleanse, integrate and analyze structured and unstructured, diverse “big data” sources to generate actionable insights and solutions using machine learning and advanced analytics. Interprets and communicates insights and findings from analyses and experiments to other analysts, data scientists,   team members and business partners.  

The Main Responsibilities

Support the development of end-to-end analytics solutions by assisting in the design and implementation of solutions that cover the entire data science lifecycle, including data discovery, cleaning, exploratory data analysis, model building, and deployment. Assist with operationalizing models and participate in the iterative process of refining models and insights based on feedback and business requirements.  

• Analyze data and build predictive, prescriptive, and advanced analytical models in various areas including capacity planning, effect/anomaly detection, predictive asset failure/maintenance,   workload optimization, customer segmentation and business performance.
• Gain direct experience with various modeling techniques such as clustering, regression, and time series forecasting, applying these techniques to generate actionable insights and recommendations.  
• Mine information for previously unknown patterns and insights hidden in these assets and leverage them for competitive advantage.  
• Create compelling data visualizations and dashboards to effectively communicate findings to both technical and non-technical audiences. Present insights in a clear, concise, and actionable manner.  
• Collaborate within and across cross-functional teams, working closely with data engineers, data scientists, and business stakeholders to understand business problems, gather requirements, and communicate insights effectively.
• Contribute to collaborative problem-solving sessions and agile development processes.  
• Develop and operationalize end-to-end machine learning pipelines on Databricks, including feature engineering, model training, evaluation, and deployment.
• Implement and manage MLOps practices, integrating Git for version control, CI/CD pipelines for model deployment, and automated monitoring of models in production.
• Develop and consume RESTful APIs for data integration, enabling seamless connectivity between analytics applications and external systems.
• Ensure reproducibility, auditability, and governance of data science models by adhering to enterprise MLOps standards and frameworks.
• Support analytics democratization by packaging models as reusable components and APIs for consumption across the enterprise.
  
  

• Able to apply techniques such as classification, clustering, regression, deep learning, association, anomaly detection, time series forecasting, Hidden Markov models and Bayesian inference to solve pragmatic business problems.  
• Able to design working models and implement them on Big Data systems using Map Reduce or Spark frameworks.  
• Familiar with Hadoop, Pig, Hive, Scope, Cosmos, or similar technologies.  
• Able to work within an agile, iterative DevOps development process.  
• Experience: 3+ years of experience delivering Machine Learning and Advanced Analytics solutions  
• Experience with statistical programming environments like Python, R, SPSS, or IBM Watson Studio
• Experience building data models and performing complex queries using SQL
• Experience performance tuning large datasets
• Experience building large data pipelines and/or web services
• Experience developing visualization and dashboards using PowerBI or similar tools
• Fluent in one or more object-oriented languages like C#, C++, Scala, Java, and scripting languages like Python or Ruby