Ambition Data is seeking a data scientist with customer or marketing analytics expertise who can review large data sets, apply models and make recommendations to clients. Your data science skills should be 50% data engineering knowledge, 30% data visualization, 10% cloud and 10% machine learning. This position requires strong executive consulting skills and business expertise as well as an entrepreneurial mindset for product creation.

This position works directly with our CEO in our Portland, OR office.


The skills we would most value in you:

  1. You can find the signal in the noise by triangulating a knowledge of business value, data science, and marketing expertise
  2. You have used machine learning models and know when each is appropriate to use (e.g., random forests, gradient-boosted trees, neural networks, survival models, Bayesian inference, k-means clustering) as well as what is required to use them
  3. You are able to meaningfully visualize data for discovery
  4. You can define the right tools, techniques, and technologies
  5. You want to grow, manage, and inspire a small team of new data scientists
  6. You present well and even connect with clients
  7. You can be directly billable to clients when necessary
  8. You are insatiably curious with proactive problem-solving skills


Supporting skills you should have:

  • Deep familiarity with marketing analytics tools (Google Analytics, Adobe Analytics)
  • Deep familiarity with data ETL (Alteryx, Python, Stitch)
  • Some familiarity with open source tools: GitHub models, IBM models, econometric data
  • Some familiarity with email marketing tools (SFMC, Klaviyo, Marketo)
  • Some familiarity with text analysis tools, including social media analysis
  • Some familiarity with scalable software application design
  • Some familiarity of distributed computation (e.g., Spark, Presto, Hadoop)
  • Some familiarity with cloud services (e.g., AWS S3, EC2, Lambda, API Gateway, Redshift)
  • Some familiarity with databases both relational (e.g., MySQL, Postgres) and non-relational (e.g., MongoDB, Cassandra)


Where we want to go with those skills:


  • Be able to run a ‘discovery’ phase (with the support of the broader team) where you help establish trust with the client and also dive into client data, advise on data platforms and pipelines to assess the feasibility of the various customer analysis strategies clients are considering (and some they are not).
  • Identify the best quick wins in terms of customer data strategy so clients (1) learn what is possible with their data, and (2) create clearer projects with well-understood milestones, deliverables, costs, and expected ROIs.


  • Revolutionize the consulting model. We want to be less people doing labor-intensive work and more people probing for the softer “small data” points that drive the accuracy of a model.
  • Attract and build a team who can take on more specific tasks
  • Operationalize products around those models which we can sell and resell to clients.