RRemoteville

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Data Scientist

Skills
Computer ScienceSQLCreativeData AnalyticsData ModelingData ModelsData Science
Role

What the job involves

The main requirements, responsibilities and hiring steps.

Requirements

  • MS or PhD in Computer Science, Engineering, Sciences, Applied Mathematics, or a related field
  • Strong knowledge and understanding of current ML and AI technologies and platforms
  • Knowledge of current technologies and ML frameworks (tensorflow, pytorch, sklearn, keras, etc.)
  • Hands-on experience with data engineering/processing frameworks (databricks, spark, dataflow, etc.)
  • Data Science prototyping experience
  • Results-driven with a positive can-do attitude
  • Creative problem-solving skills
  • Experience with SQL
  • Excellent verbal and written communication skills
  • Prefer experience with distributed storage and computing tools such as Hadoop and Spark
  • Prefer working knowledge of streaming data solutions

Nice to have

  • Results-driven
  • Positive attitude
  • Creative
  • Excellent communication skills

Day to day

  • Perform data analysis and offer insights to inform business decisions
  • Work collaboratively with cross-functional teams to identify and address intricate data challenges
  • Create and deploy data models and algorithms to enhance decision-making processes
  • Employ programming languages and tools for extracting, cleansing, and manipulating extensive datasets
  • Present findings and recommendations to stakeholders using visualizations and presentations
  • Stay current with industry trends and advancements in Data science
  • Take charge of projects, ensuring timely completion in a dynamic work environment
  • Engage in training and developmental activities to augment skills and knowledge
  • Adhere to data privacy and security protocols to uphold the confidentiality of sensitive information
  • Perform the full data modeling and algorithm development cycle: modeling, training, tuning, validating, deploying, and maintaining services
  • Research and develop novel statistical approaches and machine learning/deep learning models that add value to manufacturing processes and product performance improvement
  • Assess the effectiveness and accuracy of data sources and data-gathering techniques