Remote Data Science and Data Analyst Jobs

Data Scientist

Artifact - US
Full-time
Posted: 6 months ago

We’re growing our team at Artifact so we can entrench our product market fit, build scalable and delightful products, and get them into the hands of the right qualified customers. Artifact helps teams centralize qualitative data into a single source of truth, and uses advanced but proven AI methods to synthesize actionable insights so they can instantly understand what’s most important to their customers.

Artifact is looking for data scientists and NLP researchers at various skill levels to join our growing team.

This is a full-time role that can be held from one of our US hubs or remotely in the United States.

Key ML focus areas:

  • Natural language processing
  • Natural language understanding
  • Knowledge Graphs and Graph Neural Networks

What you’ll do at Artifact:

  • By using massive amounts of historical and realtime qualitative data, you will help research, develop and automate models for semantic understanding, text similarities, clustering, sentiment and emotion, and expressive summarization.
  • One example: Build / Enhance our end-to-end solution that analyzes historical customer conversations and answers user questions as a summarized insight. Beyond answering, allow the user to facet and pivot the data while updating the summary insight dynamically.
  • Work with product management and design roles to understand and empathize with end customer needs.
  • Build end-to-end ML solutions spanning from data access (SQL) to model endpoint (API / batch service) and deploy to cloud based staging environments (Kubernetes on GCP) by collaborating with software engineers.
  • Collaborate with our academic partners and research new ML techniques / enhancements and publish blogs or journal / conference papers.
  • Open source code or software that will benefit the larger community of ML practitioners.

We'd love to hear from you if you:

  • Have solid fundamentals in approaching and solving ML / DS problems. Think: "data source", "data lineage", "data transformation", "data age and relevance", "model consumption", "post consumption action" etc.
  • Lean toward action and experimentation with a priority towards rapid prototyping.
  • Approach problems from a first principles mindset and question assumptions.
  • Have dived deep and spent a good amount of time solving "one" ML problem.
  • Are naturally curious and willing to take a step to learn something they don’t have experience in.
  • Have experience with SQL, Python, NLTK or Spacy or other NLP libraries, PyTorch or JAX or TensorFlow. DGL and data visualization experience is a plus.

Experience Bonus:

  • Masters Degree or PhD in Computational Linguistics, Engineering, Statistics or related field
  • Previous experience/education in syntax, semantics and morphology
  • Experience with semantic understanding and summarization of short phrases across a heterogenous corpus.
  • Experience collaborating with software development teams, data scientists, business intelligence or other technical roles

Artifact Memes (How we behave at work...)

  • Transparently documented communication: we leave documented breadcrumbs in every decision we make so that those we collaborate with in the future have the necessary context, and can adequately support us in future decision making.
  • Tiny, frequent, and constantly kept agreements: every single day, we break down initiatives into tiny agreements we make with our team that we are 100% certain we can keep.
  • Strategy before story telling: before we build narratives for our vision and initiatives we prioritize the process of establishing what we’ll do next, why that is the correct thing to do next, and identify risks and trade-offs.
  • Focus on good decision making and long-term results: we believe strongly that you ****are not the results of a single decision, nor do the results of a single decision deride your ability to make sound decisions. We are long, bullish, and team You— and focus on the results of consecutive intentional decisions.
  • Systems thinking: we have seen most objective-result organizational behaviors result in fleeting meaningless goals. Rather than a goals-based mindset, we ask you to develop an outcomes-based and diagnostics-based mindset.
  • Establish clear lines of responsibility: Document what you're trying to accomplish, why you're trying to accomplish it, and who will be responsible for specific initiatives.
  • Practice proactive accountability: we ask you to practice introspection and proactively assume accountability for your decisions.
  • Remove your ego: when your ego is involved you become so internally absorbed that it's difficult to level-up and see what's happening and how you're behaving. Check it at the door.
  • Work cross functionally: We don’t tolerate, support, or allow employees, teams, or departments to prioritize job descriptions over the success of the business. We are building a business. We are not here to build a pipeline, close opportunities, design products, or write code or algorithms. We are building a business. Work together to accomplish this outcome.
  • Identify risks and assumptions: be honest and self aware with yourself and your team about what you don’t know. Assess your risks. Identify what you need to learn. Then dive in and do your best work.
Data Science and Analyst