Data Scientist II


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Located in Bend, OR

Salary: Competitive comp & benefits

We have a collaborative, high-energy work environment where team members are empowered to “run with” ideas to improve processes. You will play a key role in transforming structured and unstructured data into insights and models for business decision-making. We look for candidates who are not satisfied with the status quo, are intellectually curious and confident in their abilities. If you are looking to join a dynamic, exciting and growing leader, consider joining our team!

Where You Get to Live!

Bend, Oregon is the mountain town that has it all!  Located in the in the shadows of the Cascade Mountains and surrounded by numerous lakes and rivers, Central Oregon is an outdoor enthusiast’s paradise. Check out the sights and sounds of Bend at:

What You Get to Do!

The Data Scientist II performs individual work assignments, participates in working groups and contributes to enterprise projects, often independently representing the Data Science and BI Team. The Data Scientist II has a strong business acumen and ability to frame business problems and transform them into analytical problems to be solved using appropriate data science methods. The Data Scientist II has a breadth of expertise in data science methodologies and techniques and can select appropriate tools for accessing and cleansing data, develop code, build predictive models, and apply statistical methods to achieve solutions to be validated by the business. This position requires minimal supervision delivering outcomes, a high breadth/depth of job specific knowledge and an advanced level of service delivery, professionalism, and communication.

Conduct data science:

  • Lead discovery processes of high complexity with stakeholders to define the business problem, understand IT and business constraints and opportunities and understand the qualitative nature of data required to deliver results.
  • Transform the business problem into an analytical problem and identify a wide breadth of data science approaches for achieving the desired business insights and criteria for selecting among approaches.
  • Build data pipelines from sources including internal data (i.e., point-of-sale, ERP and financial systems, websites, etc.) and external data (i.e., weather stations, geo-location systems and social media sites).
  • Apply data cleansing techniques such as deduplication, hashing, scaling and normalization, dimensionality reduction, fuzzy matching, imputation and cross-validation.
  • Design experiments to gain insight and test hypotheses using quantitative methods.
  • Apply various Machine Learning (ML) and advanced analytics techniques to perform classification or prediction tasks.
  • Present insights and rationale of recommendations in easy to understand terms; guide business stakeholders to validate insights and recommendations; maintain an ability and willingness to present analysis results that are data driven and may contradict common belief.
  • Collaborate with data engineers and IT to evaluate and implement deployment options for developed models.
  • Identify the lifecycle of any developed models and insights and develop maintenance plans for ongoing operational use of insights and recommendations.

Contribute to Data Science and BI Team effectiveness:

  • Assist the Data Science and BI team lead in creating high quality summaries of Data Science projects and results for presentation to steering committees and executive groups
  • Assist the Data Science and BI team lead in scoping and prioritizing data science projects
  • Create reusable artifacts and contribute to data and insight catalogues and documentation
  • Be a lead participant in peer reviews and presentation of specialist data science topics to advance collective team understanding of relevant technologies and techniques to accomplish data science outcomes
  • Network within IDS and business partner departments to gain business understanding
  • Proactively engage in continuous professional improvement in both technical and soft skills

Contribute to BI Portfolio effectiveness:

  • Partner with data stewards and data platform developers in continuous improvement processes to help improve data quality
  • Recommend ongoing improvements to data capture methods, analysis methods, mathematical algorithms, etc. that lead to better outcomes and quality.
  • Contribute to group retrospectives and improvement of processes for collective work management
  • Help improve enterprise stakeholder understanding of related technologies and processes to accomplish data science outcomes
  • Guide and inspire others about the potential applications of data science

What You Will Need:

  • Bachelor’s degree in applied mathematics, statistics, computer science, operations research, or a related quantitative field.  Alternate experience and education in equivalent areas such as economics, engineering or physics is acceptable. 
  • Master’s degree preferred 
  • Certified Analytics Professional credential (available through INFORMS.ORG) required
  • AND minimum of 3-6 years of full-time or equivalent relevant experience executing data science projects, preferably in the domains of customer behavior prediction and operations management.
  • Advanced coding knowledge and experience in at least two programming languages: for example, R, Python/Jupyter, C/C++, Java or Scala.
  • Advanced knowledge of database programming languages including SQL, PL/SQL, or others for relational databases, graph databases or NOSQL/Hadoop-oriented databases.
  • Broad Knowledge and experience in statistical and data mining techniques that include generalized linear model (GLM) / regression, random forest, boosting, trees, text mining, hierarchical clustering, neural networks, graph analysis, data sampling, design of experiments, etc.  Familiarity with typical algorithms used by retail businesses (i.e., Churn, Segmentation) required.
  • Technical skills for working across multiple deployment environments including cloud, on-premises and hybrid and skills for acquiring new datasets, parsing datasets, organizing datasets, representing data visually and automating data-driven models. 
  • Advanced knowledge of statistical tools and advanced analytics platforms such as: Minitab, SAS, Knime, Dataiku, Anaconda, Google Collaboratory

To Apply: For confidential consideration, please submit resume to:

Express Office: Bend
61379 South Highway 97
Bend, OR 97702
Apply Now