Data Scientist

Role: DATA SCIENTIST

The Role

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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.

What We Look for in a Candidate

• 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

"We are an equal opportunity employer committed to fair and ethical hiring practices. We do not charge any fees or accept any form of payment from candidates at any stage of the recruitment process. If anyone claims to offer employment opportunities in our company in exchange for money or any other benefit, please treat it as fraudulent and report it immediately."
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