Data Scientist

Role - Data Scientist

Location - Remote, Poland

Type - B2B Contract and Contract of Employment (UoP)

Job Description:

Key responsibilities

Technology

• Must know the in and out of the AI/ML algorithm being used and not just the library to call.
• Should have commissioned atleast two projects end to end beyond the Proof of Concept.
• Design state of the art descriptive, diagnostic and predictive algorithms based on the business requirements
• Deliver enterprise level analytics solutions to SLB customers
• Provide analytics solutions in Big data platform which can scale
• Design distributed, GPU and parallelized algorithms
• Architect the data science solutions to complex high dimensional data
• Deep dive on to theoretical and implementation details and provide the solution fundamentals
• Support architecture and platform team to optimize their operation
• Capable to train and gain expertise quickly on new Infrastructure tools and all assigned technologies

Business

• Engage with the business segments and internal stakeholders to capture requirements
• Work closely with analysts and business process managers to develop an end-to-end analytics solution
• Present and Communicate solution design with Business & IT audience

External Presence

• Engage with the external technical and business community to brand and differentiate analytics best practices in Schlumberger
• Participate in technical forums and other appropriate events and conferences

Qualifications and Requirements

Essential qualifications

• PhD or Master’s degree in Computer Science, Electrical Engineering, Mathematics and Computing, Operations Research from top tier institutes.
• Devops driven 4-6 years of Industrial experience in developing core machine learning algorithms
• Academically trained in the field of machine learning/AI/Data Science/Computer Vision etc
• Excellent communication, verbal and written skills

Key competencies in data science technologies

• Strong background in some of these listed areas: Machine learning, time series analysis / sensor processing, AI/Deep Learning, optimization/operations research, text analytics/NLP, Applied Mathematics, Decision Sciences, Computer Vision
• Building and applying machine learning / predictive modelling in real-world use cases
• Strong understanding and implementation and solution architecting of predictive / analytical modeling techniques, theories, principles, and practices
• Strong theoretical foundations in machine learning, optimization, stochastic process, linear algebra etc.
• Excellent knowledge of data mining / predictive modeling tools such as Python, Spark, Tensorflow, Keras, MLlib etc.
• Ability in designing distributed and parallelized algorithms
• Ability in deploying real world solutions in Hive, Spark and other Hadoop Technologies in Cloud and In-premise
• Capable of delivering on multiple competing priorities with little supervision