Jr. Data Scientist

Well Data Labs – DENVER, CO



Python, R - Familiarity with SQL, Keras, Tensorflow, Spotfire, AWS, Linux, Hadoop, Scala, Java or C++



Salary : $65000 - $100000  / YEAR

Well Data Labs (WDL) is a team focused on bringing modern software design and usability to the oilfield. Our product is a modern web application that manages, analyzes, and reports frac data for Completions Engineers and other industry experts. We work side-by-side with our customers to give them superpowers! Well, maybe not the kind of superpowers Marvel characters might have but, super powers for Completions Engineers - analyzing data in seconds!

 

Company Culture: We believe that culture is one of the most important parts of a healthy workplace. It is a major factor in your overall enjoyment of life, and it leads to better trust, transparency, and Productivity. Culture can sometimes be referred to in fluffy, airy terms that mean very little in reality. Here at Well Data Labs, our culture is derived from our Core Values.

WDL Core Values

Create value

Act with integrity

Embrace transparency

Deliver results

Strike a balance

Celebrate as a team

About this position:

We are looking for a data lovin’, independent but collaborative, self-driven, Data Science Intern to join our Research and Development team. WDL research team uses machine learning to automate the analysis and recognition of events in time-series metered data generated in the field. This role requires the ideation and prototyping of robust, production-level machine learning solutions

This is a paid internship with the intent to hire this person on full time! Please apply only if you have the intent to join our team full-time.

Well Data Labs offers paid parking, a casual office, stocked kitchen and flexible schedules.

Characteristics & Mindset of a Successful Candidate:

Insatiable curiosity for data and machine learning

Collaborative self-starter who takes initiative and can self-manage

Analytical mind, problem-solving aptitude, and business acumen

Responsibilities & Opportunities of the Role:

Work closely with the Product and Engineering teams on designing and implementing machine learning models that address key customer and market needs and that are integrated with key product features

Undertake preprocessing of structured and unstructured data

Analyze large amounts of information to discover trends and patterns

Process, clean, and verify the integrity of data used for analysis

Propose solutions and strategies to business challenges

Create maintainable projects with documentation

Do ad-hoc analysis and present results in a clear manner

Learn the intricacies of the completions data life-cycle (Don’t worry, we have engineers, scientists and product experts that will teach you everything you need to know!)

Minimum Qualifications to Apply:

Graduate student working toward a degree in Petroleum Engineering, Computer Science or Mathematics/Statistics. Recent graduates will be considered.

Strong conceptual knowledge and background in probability, applied statistics, data mining, and machine learning

Experience with statistical programming and structured data development

Well-versed knowledge of Python, R - Familiarity with SQL, Keras, Tensorflow, Spotfire,  AWS, Linux, Hadoop, Scala, Java or C++ is a plus

Understanding of modern machine learning techniques and their mathematical underpinning, such as classification, recommendation systems, optimization, deep learning, and natural language processing. Knowledge of time-series signal processing is a plus

Excellent written, communication and presentation skills

Why we really like working here:

Family-friendly company with leadership that encourages a life outside of work (we don’t work late nights or weekends)

Opportunity for real-world experience and career growth beyond the Internship

Awesome office in LoDo (Downtown Denver)

Focus on a culture of diversity (of thought and background)

We&39;ve been awarded one of the Best Places to Work by Denver Business Journal!

And we work from home every Friday!

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