| Data Scientist- Mid level at Remote, Remote, USA |
| Email: [email protected] |
| From: Farha khan, Tek Inspirations LLC [email protected] Reply to: [email protected] Job Description - Role: Data Scientist- Mid level Corteva Agriscience Hybrid- Indianapolis, IN Interview Style: 2-3 interviews virtual Need LinkedIn KEY SKILLS: Machine Learning R or Python Development CI/CD pipeline development Docker, Podman, Kubernetes, GIT Modeling biological, cellular, or ecological data Data Science in AGRICULTURE Desired Role & Level: Role / Level: Data Scientist / mid Location: Indianapolis, IN candidate living within 50 mile radius of location required onsite T/W/TH each week. Remote - candidate can be considered as remote if in the US and living more than 50 miles from above indicated GBC locations. Project Scope and Brief Description: The candidate must have experience and fundamental knowledge in machine learning, experience in deploying models, and programming skills to develop and deliver novel solutions in an industry setting. Responsibilities: Partner with R&D scientists to develop and prototype rigorous machine learning solutions aligned to project needs Design and implement scalable data pipelines for processing high-complexity datasets such as high-throughput bioassays or large-scale agriculture datasets Partner with data scientists, data engineers, and production teams to deploy and maintain data products at scale Communicate and train research partners on models and products to facilitate data-driven decisions Communicate insights derived from complex data analysis into simple conclusions that empower leadership to drive action; communicate results in internal and external forums; and contribute to scientific articles as needed Steward data product life cycle and partner with other scientists to continuously improve underlying models and optimize data architecture Stay abreast of emerging technologies in big data, machine learning, and agriculture tech and advocate for their adoption where beneficial Skills / Experience: Educational Qualifications M.S. or above in Applied Statistics, Artificial Intelligence, Biostatistics, Computer Science, Data Engineering, Data Science, Engineering, Machine Learning, Physics, Software Engineering, or related highly quantitative fields. Ph.D or additional years of experience preferred but not required. Required Qualifications Strong expertise in R or Python programming languages and their application to data wrangling, machine learning (e.g., TensorFlow, PyTorch), and data visualization Experience and fundamental understanding of machine learning techniques (e.g., logistic regression, random forest, XGBoost, SVMs, K-means, neural networks) Solid understanding of variable selection; dimensionality reduction; model diagnostics; and model training, testing, and validation Experience deploying machine learning models in production (e.g., CI/CD pipeline development; containerization using tools such as docker, podman, or Kubernetes; Git) Ability to work both independently and within a multidisciplinary team environment to provide innovative solutions Ability to successfully collaborate with colleagues from diverse technical backgrounds which includes excellent communication, interpersonal, verbal, and written skills Strong critical thinking and problem-solving skills, flexibility, and willingness to learn Preferred Qualifications: Familiarity with modeling biological, cellular, or ecological data; molecular biology or biochemistry concepts; or data science in agriculture Proven experience as a machine learning engineering or similar role with a strong focus on machine learning deployment and data pipeline construction Familiarity with artificial intelligence or generative AI techniques Experience in big data technologies (e.g., Hadoop, Spark) and database management systems (e.g., SQL, NoSQL) Experience with AWS Experience consulting on scientific projects or working within a scientific team Keywords: continuous integration continuous deployment artificial intelligence rlang Data Scientist- Mid level [email protected] |
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| Mon Apr 01 23:09:00 UTC 2024 |