The Data Science candidate primary role is to act as a Data Science Architect and will be required to develop and investigate hypotheses, structure experiments and build and maintain mathematical models for predictive and text analytics. The candidate will be involved in all phases of analytics projects including: question formulation, design, research and development, implementation, testing, etc. The candidate must have a proven ability to drive business results with their data-based insights. He must be comfortable working with a wide range of stakeholders and functional teams. The right candidate will have a passion for discovering solutions hidden in large data sets and working with stakeholders to improve business outcomes.
Duties & Responsibilities:
You will be required to perform the following:
- Gather and refine specific business scenarios and document analytical solution requirements.
- Develop and plan required analytic projects in response to business needs and maintain documentation of technical requirements including use cases.
- Define the data requirements for specific business scenario and identify sources of data in consultation with business clients and technical team.
- Identify and resolve data/information gaps for meeting business objectives/goals including data quality issues and sources (data feeds, the master data, etc.), and identify training and validation datasets as appropriate.
- Build analytical models to execute specific business scenarios using all available tool sets.
- Conduct research and make recommendations on data mining products, services, protocols, and standards in support of procurement and development efforts.
- Provide and apply quality assurance best practices for data mining/analysis services.
- Evaluate and prototype innovative solutions using the latest technology trends.
- Use scientific /analytical approaches to explore and visualize available data, identify gaps, and build/validate required analytic models.
- Determine required network components to ensure data access, as well as data consistency and integrity.
- Respond to and resolve data mining performance issues. Monitor data mining system performance and implement efficiency improvements.
- Manage and/or provide guidance to junior members of the team
- Acquire good scripting and programming skills
- Ability for processing, cleansing, and verifying the integrity of data used for analysis
- Develop prototypes, proof of concepts, algorithms, predictive models, and custom analysis
- Communicate predictions and findings to management and IT departments through effective data visualizations and reports
- Recommend cost-effective changes to existing procedures and strategies.
As the successful candidate, Bachelor’s degree in one of the following disciplines; Safety Engineering, Industrial Engineering, Applied Mathematics, Statistics, or Computer Science from a recognized and approved program. An advanced degree is preferred.
12 years of experience in oil and gas experience with at least 5+ years of hands-on experience in data science including at least 3+ years in statistical methods, such as machine learning (ML), information retrieval, data mining, text Mining, predictive analytics, and geo-spatial analytics.
- Experience in predictive analytics and machine learning using R, Python.
- Experience in applying ML to real-world problems.
- Deep exposure in statistics and statistical analytics and concepts.
- Knowledge on SQL and database querying language.
- Data visualization experience is a requirement.
- Data mining, data cleanup, and data massaging experience is a requirement.
- Ability to analyze data sets (structured/unstructured) and build sophisticated mathematical/statistical models.
- Knowledge in math like linear Algebra, calculus and probability is a requirement.
- Ability to demonstrate project work with evidence of creative and critical thinking.
- Analytical, problem solving and organizational skills.
- Excellent English language communication skills (verbal, presentation and written), including the ability to collaborate and influence effectively across all levels of a matrix organization is also necessary.
- Ability to show Knowledge of big data tools like Hadoop, Hive and Pig, and cloud tools such as Amazon.