DATA SCIENTIST

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Location: MUMBAI, India
Job Family: Engineering
Updated At: 2020-06-17

ORGANIZATION SUMMARY

Weatherford
Weatherford is one of the largest multinational oilfield service companies providing innovative solutions, technology and services to the oil and gas industry. The Company operates in more than 80 countries and has a network of approximately 700 locations, including manufacturing, service, research and development, and training facilities and employs approximately 20,000 people.
Weatherford delivers innovative technologies and services designed to meet the world’s current and future energy needs in a safe, ethical, and sustainable manner. Grounded by our core values and inspired by our world-class people, we are committed to being a trusted business partner to those we serve.

JOB DESCRIPTION

Job Purpose
Weatherford’s Production business is seeking a Data Scientist who is passionate about data and wants to apply machine learning techniques to solve problems for our customers.   This person is expected to be proficient in the exploration and understanding of structured and unstructured data, machine learning techniques, statistical modeling methods, predictive analytics, anomaly detection, and supervised and unsupervised learning.   The successful candidate will work with stakeholders to leverage data to solve critical business problems in the oil & gas production domain.
The Data Scientist will work on all aspects of the design, development and delivery of machine learning enabled solutions including problem definition, data acquisition, data exploration, feature engineering, experimenting with various ML algorithms, evaluating metrics, deploying models and iteratively improving the total solution.   He or she will work with data from diverse, unstructured formats including numerical, time series, text, and image.
 
Duties & Responsibilities 
 
Formulate meaningful hypothesis that are relevant to the business objectives.
Design and train models for use in production environments.
Mine structured and unstructured data for patterns.
Utilize data from databases, historians, and/or data lakes.   
Rigorously build, analyze and compare machine learning or statistical models; there is a strong emphasis on programming using the most popular machine learning languages such as Python.
Work with application developers to develop data-analytics products that are deployed to end-users as part of packaged solutions.
Visualize and report findings of deployed data analytics solutions to provide insights to the organization and our customers.
Deploy machine learning model and integrate model predictions in business
Setup infrastructure for machine learning, model deployment
Deploy CI/CD framework to frequently deliver code/features to production
 

QUALIFICATIONS

Experience & Education
 
Required:
B.S. or higher in Engineering, Mathematics, Statistics or Computer Science with significant experience in data analytics.
 
Preferred:
MS degree with 5+ years’ experience is preferred.
 
Knowledge, Skills, & Ability
 
Required:
Expertise in predictive modeling, machine learning and statistics.
Software development skills in one or more high level languages (Python/Java/R/Scala).
Experience using one or more of the following common ML software packages:  scikit-learn, TensorFlow, NumPy, pandas, jupyter.
Well-versed in machine learning algorithms and their suitability for solving various problems: Regression, Bayesian, Support Vector Machines, Decision Trees, Random Forest, Clustering, Neural Networks.
Experience in using SQL/No SQL databases is an advantage
Experience working in Linux is an advantage
Experience with Big Data technologies is an advantage (Hadoop, Hive, Spark, Cassandra).
Experience with building and deploying data pipelines
Good critical thinking, technical, data collection and user interviewing skills.
Ability to work as a team member in a fast-paced environment.
Experience with Agile software development processes is preferred.
Experience with Cloud service offerings from AWS, Azure or GCP is a plus.
 
Preferred:
Knowledge of DataOps.
Knowledge of data versioning tools such as git, DVC
Knowledge of ML environments such as MLflow, databricks
Knowledge of ML deployment tools such as Kubeflow, Kubernetes
 

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