W. R. Grace

Sr Data Scientist

Job description

Requisition ID: 22003

Built on talent, technology, and trust, Grace is a leading global supplier of catalysts and engineered materials. The company’s two industry-leading business segments—Catalysts Technologies and Materials Technologies—provide innovative products, technologies, and services that enhance the products and processes of our customers around the world. Grace employs approximately 4,300 people in over 30 countries.

Job Description

The Sr. Data Scientist is part of the India Digital Operations Center and will be responsible for developing predictive models (catalyst quality as a function of operating variables, Reliability and maintenance metrics etc) & optimization that will drive improvement in Grace manufacturing capabilities across the world. Critical to success will also be requirements for the person to recommend ways to improve quality, reliability, and efficiency.

  • Understand business use of data and stakeholder requirements to support work processes and strategic business objectives.
  • Leverage data, software engineering, and data science techniques to create business value through data accessibility.
  • Develop data-driven decisions and insights using predictive models to support grace operations & monitor the performance of assets as a function of parametric adjustment for manufacture excellence
  • Analyze large volumes of internal and external data using common data science tools (Python, SQL/NoSQL etc.) & various ML Models to deliver valuable insights for operational excellence.
  • Effectively summarize & communicate complex analytic results to a variety of audiences.
Required Experience
  • Good knowledge and experience in handling large volume data sets using Programming languages (python, SQL & other script languages) & various ML models to develop predictive models to make operational improvement decisions/business decisions.
  • Experience with cloud environments: data lake, data warehousing, Google Cloud preferred.
  • strong experience in advanced analytics, model building, statistical modeling
  • Good knowledge and experience in handling & implementing ML models such as GLM, NB, SVM, LDA, K-means. Experience working in Python and various libraries such as Pandas, Numpy, Ski-kit learn, etc is mandatory.
  • Expert knowledge of advanced statistical techniques like Decision Trees, regression, correlation.
  • excellent track record of building Statistical and Deep Learning models and implementing them into production
  • Good understanding and hands-on experience with time series and forecasting modelling.
  • Develop an optimized solution from predictive models & convert them into tool-based solution for operation/business team leaders
  • Experience in Preventive maintenance predictive models, equipment reliability analysis models are preferable but not mandatory.
  • Knowledge and/or experience with data acquisition, preparation, and validation
Required Skills
  • Ability to quickly learn details of Grace’s chemical processes and identify the impact of automation improvements on the cost, quality, and safety of the manufacturing operation.
  • Grace organization and external resources, including management of vendor relationships
  • Outstanding analytical skills including financial business case creation
  • Strong interpersonal skills needed to manage and collaborate with multiple internal resources within the
  • Strong written and verbal communication skills
Required Qualifications

BS in Engineering (Chemical) with minimum of 5 years prior experience as Data Engineer level role or BS in Computer Science with minimum of 5 years prior experience in Chemical Manufacturing

Other Compensation

Grace is not accepting unsolicited assistance from search firms for this employment opportunity. Please, no phone calls or emails. All resumes submitted by search firms to any employee at Grace via email, the Internet or in any form and/or method without a valid written search agreement in place for this position will be deemed the sole property of Grace. No fee will be paid in the event the candidate is hired by Grace as a result of the referral or through other means.

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