Job
Description
About Oceaneering: Oceaneering is a global provider of engineered services and products, primarily to the offshore energy industry. We develop products and services for use throughout the lifecycle of an offshore oilfield, from drilling to decommissioning. We operate the world's premier fleet of work class ROVs. Additionally, we are a leader in offshore oilfield maintenance services, umbilicals, subsea hardware, and tooling. We also use applied technology expertise to serve the defense, entertainment, material handling, aerospace, science, and renewable energy industries. Role Summary: The Principal Data Scientist will develop Machine Learning and/or Deep Learning based integrated solutions that address customer needs such as inspection topside and subsea. They will also be responsible for development of machine learning algorithms for automation and development of data analytics programs for Oceaneerings next generation systems. The position requires the Principal Data Scientist to work with various Oceaneering Business units across global time zones but also offers the flexibility to work in a Hybrid Work-office environment. Responsibilities: Lead and supervise a team of moderately experienced engineers on product/prototype design & development assignments or applications. Work both independently and collaboratively to develop custom data models and algorithms to apply on data sets that will be deployed in existing and new products. Mine and analyze data from company databases to drive optimization and improvement of product development, marketing techniques and business strategies. Assess the effectiveness and accuracy of new data sources and data gathering techniques. Build data models and organize structured and unstructured data to interpret solutions. Prepares data for predictive and prescriptive modeling. Architect solutions by selection of appropriate technology and components Determines the technical direction and strategy for solving complex, significant, or major issues. Plans and evaluates architectural design and identifies technical risks and associated ways to mitigate those risks. Prepares design proposals to reflect cost, schedule, and technical approaches. Recommends test control, strategies, apparatus, and equipment. Develop, construct, test, and maintain architectures. Lead research activities for ongoing government and commercial projects and products. Collaborate on proposals, grants, and publications in algorithm development. Collect data as warranted to support the algorithm development efforts. Work directly with software engineers to implement algorithms into commercial software products. Work with third parties to utilize off the shelf industrial solutions. Algorithm development on key research areas based on clients technical problem. This requires constant paper reading, and staying ahead of the game by knowing what is and will be state of the art in this field. Ability to work hands-on in cross-functional teams with a strong sense of self-direction. REQUIRED Bachelors degree in Electronics and Electrical Engineering (or related field) with ten or more years of past experience working on Machine Learning and Deep Learning based projects OR Masters degree in Data Science (or related field) with eight or more years of past experience working on Machine Learning and Deep Learning based projects DESIRED Strong knowledge of advanced statistical functions: histograms and distributions, Regression studies, scenario analysis etc. Proficient in Object Oriented Analysis, Design and Programming Strong background in Data Engineering tools like Python/C#, R, Apache Spark, Scala etc. DESIRED Experience/Knowledge: Strong knowledge of advanced statistical functions: histograms and distributions, Regression studies, scenario analysis etc. Proficient in Object Oriented Analysis, Design and Programming Strong background in Data Engineering tools like Python/C#, R, Apache Spark, Scala etc. Prior experience in handling large amount of data that includes texts, shapes, sounds, images and/or videos. Knowledge of SaaS Platforms like Microsoft Fabric, Databricks, Snowflake, h2o etc. Background experience of working on cloud platforms like Azure (ML) or AWS (SageMaker), or GCP (Vertex), etc. Proficient in querying SQL and NoSQL databases Hands on experience with various databases like MySQL/PostgreSQL/Oracle, MongoDB, InfluxDB, TimescaleDB, neo4j, Arango, Redis, Cassandra, etc. Prior experience with at least one probabilistic/statistical ambiguity resolution algorithm Proficient in Windows and Linux Operating Systems Basic understanding of ML frameworks like PyTorch and TensorFlow Basic understanding of IoT protocols like Kafka, MQTT or RabbitMQ Prior experience with bigdata platforms like Hadoop, Apache Spark, or Hive is a plus.
