Job Description :
On behalf of one of our large Oil & Gas clients in LaPorte TX, Procom Services is looking for a Junior Data Scientist for a fulltime perm direct hire opportunity.
Please review this requirement and If interested/know of someone who would be interested in this role then email your updated resume in a word document with your contact details, current location, availability and salary expectations to
Job Title DATA SCIENTIST
Leadership Reports to the Department Manager ; or as assigned
Job Purpose Planning, executing and delivering of descriptive, predictive and prescriptive analytics and machine learning projects
EMPLOYEES ACCOMPLISH OUR MISSION THROUGH THE DAILY EXECUTION OF OUR CORE VALUES:
SAFETY LEADERSHIP
BUSINESS ETHICS OPERATIONAL EXCELLENCE
CUSTOMER SATISFACTION FINANCIAL PERFORMANCE
TEAMWORK EMPLOYEE DEVELOPMENT
COMMUNITY/CHARITY
ESSENTIAL DUTIES AND RESPONSIBILITIES(not all inclusive)
INDIVIDUAL MUST BE ABLE TO PERFORM THE ESSENTIAL DUTIES WITH OR WITHOUT REASONABLE ACCOMMODATION
The data scientist is a role in the Information Technology department and reports to the Chief Data Officer. They will play a pivotal role in planning, executing and delivering descriptive, predictive and prescriptive analytics and machine learning-based projects. The bulk of the work will be in machine learning modelling, management and problem analysis, data exploration and preparation, data collection and integration, operationalization.
The newly hired data scientist will be a key interface between the Information Technology department and the analytics team, and various other departments. Candidates need to be very much self-driven, curious and creative. As our client is still building up its data analytics practice, the role must also cover the related roles of data engineer and DevOps engineer at times.
Primary Responsibilities Problem Analysis and Project Management
Collaborate across the business to understand IT and business constraints
Prioritize, scope and manage data science projects and the corresponding key performance indicators (KPIs) for success
(Help to) define and communicate governance principles
Primary Responsibilities Data Collection and Integration
Understand new data sources and process pipelines, and catalog/document them
Acquire access to various databases, and other source systems such as SQL or graph databases.
(Help to) create data pipelines for more efficient and repeatable data science projects
Primary Responsibilities Data Exploration and Preparation
Apply statistical analysis and visualization techniques to various data, such as hierarchical clustering, T-distributed Stochastic Neighbor Embedding (t-SNE), principal components analysis (PCA)
Generate hypotheses about the underlying mechanics of the business process
Test hypotheses using various quantitative methods
Display drive and curiosity to understand the business process to its core
Network with domain experts to better understand the business mechanics that generated the data
Primary Responsibilities Machine Learning
Apply various ML and advanced analytics techniques to perform classification or prediction tasks
Integrate domain knowledge into the ML solution; for example, from an understanding of financial risk, customer journey, quality prediction, sales, marketing
Testing of ML models, such as cross-validation, A/B testing, bias and fairness
Primary Responsibilities Operationalization
Collaborate with ML operations (MLOps), data engineers, and IT to evaluate and implement ML deployment options
(Help to) integrate model performance management tools into the current business infrastructure
(Help to) implement champion/challenger test (A/B tests) on production systems
Continuously monitor execution and health of production ML models
Establish best practices around ML production infrastructure
Primary Responsibilities Other
Train other business and IT staff on basic data science principles and techniques
Train peers on specialist data science topics
Network with internal and external partners
Upskill yourself (through conferences, publications, courses, local academia and meetups
Promote collaboration with other data science teams within the organization (if there is a decentralized data science practice Encourage reuse of artifacts
EDUCATION, EXPERIENCE, & ABILITY REQUIREMENTS
ANY COMBINATION OF REQUIREMENTS WHICH PROVIDE KNOWLEDGE & ABILITIES NECESSARY TO PERFORM ESSENTIAL DUTIES & RESPONSIBILITIES WILL BE CONSIDERED
EDUCATION AND TRAINING
A bachelor s or master s degree in computer science, data science, operations research, statistics, applied mathematics, or a related quantitative field is required. Alternate experience and education in equivalent areas such as economics, engineering or
physics, is acceptable. Experience in more than one area is strongly preferred.
Candidates must have a specialization in ML, AI, cognitive science or data science.
PREVIOUS BUSINESS EXPERIENCE
Candidates should have a minimum of three years of relevant project experience in successfully launching, planning and executing data science projects. Preferably in the domains of business process automation.
A specialization in text analytics is required.
Ideally, the candidates are adept in agile methodologies and well-versed in applying DevOps/MLOps methods to the construction of ML and data science pipelines.
In addition, for midlevel to senior roles:
Candidates should exhibit significant, project experience in applying ML and data science to business functions such as customer journey analytics, marketing analytics, quality assessment, e-commerce platforms, warehouse logistics, process control,
target marketing, churn management, etc.
Candidates need to demonstrate that they were instrumental in launching significant data science projects.
Candidates should have demonstrated the ability to manage data science projects and diverse teams.
IT KNOWLEDGE/SKILLS
Coding knowledge and experience in several languages: for example, [R, Python/Jupyter, Excel, MATLAB, etc.
Experience with popular database programming languages including T-SQL for relational databases and upcoming nonrelational databases such as NoSQL/Hadoop-oriented databases such as CosmosDB.
MACHINE LEARNING AND DATA SCIENCE KNOWLEDGE/SKILLS
Experience in one or more of the following commercial/open-source data discovery/analysis platforms: RStudio, Spark, KNIME, RapidMiner, Alteryx, Dataiku, H2O, Microsoft AzureML.
Expertise in solving text analytics, credit scoring, failure prediction, propensity to buy problems is preferable.
Knowledge and experience in statistical and data mining techniques: generalized linear model (GLM)/regression, random forest, boosting, trees, text mining, hierarchical clustering, deep learning, convolutional neural network (CNN), recurrent neural
network (RNN), T-distributed Stochastic Neighbor Embedding (t-SNE), graph analysis, etc.
INTERPERSONAL SKILLS AND CHARACTERISTICS
All candidates must be self-driven, curious and creative.
They must demonstrate the ability to work in diverse, cross-functional teams [in a dynamic business environment].
Knowledge of data modeling and data warehouse cube construction
Ability to establish a rapport with internal/external customers using written correspondence, proper phone etiquette, and other effective interpersonal skills.
ADDITIONAL QUALIFICATIONS:
Must have the ability to provide documentation verifying legal work status;
Ability to read and speak the English language proficiently in order to communicate with others, understand and interpret safety instructions, and to respond to inquiries;
Ability to understand and comply with our client s guidelines and expectations, to include Code of Conduct and Conflict of Interest guidelines.
             

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