At Falkon, we are building a new system of intelligence that empowers professionals to define, understand and improve metrics that really matter.
We are product, engineering and research veterans from Microsoft, Amazon, Dropbox, Amperity and Zulily. Having lived through years of bad metrics meetings and fire-drills, we've discovered a revolutionary way to combine machine learning and human intuition to empower professionals to define, understand and improve metrics that really matter.
We are looking to add a talented research or applied scientist to our high-velocity data science team. In this role, you will be responsible for designing and implementing the algorithms and models at the very heart of Falkon's product and work closely along side other scientists, engineers, and product managers in seeing your work implemented. As an early and critical member of our growing team you will help shape our business, our processes, and our culture.
What will you do?
- Conceive, research, and implement cutting-edge models and algorithms that powering the next generation of business intelligence, unlocking insight and action from vast amounts of data.
- Partner with product and engineering to bring your models into production
- Analyze complex, high-dimensional data, including time series, structured, and unstructured data, across a range of applications and use cases
- Propose novel technical solutions to some of the most challenging problems confronting companies around making truly data-driven decisions, and see those proposals brought to life.
What you'll need to succeed:
- Expertise, through research or industry, in any of the following fields: supervised or unsupervised machine learning, causal inference, or time series forecasting/analysis
- Past experience working in a production code base and ability to assist in bringing models into production and debugging their outputs
- Strong proficiency in Python
- Ability to proactively solve issues around data access and quality, and familiarity working with data pipelines
- Excitement to translate challenging, ambiguous problem statements into models and algorithms
- Readiness to learn something new every day, collaborate with other scientists and engineers, and work in a fast-paced, dynamic start-up environment
- MS or PhD in engineering, statistics, economics, or related field with 3+ (PhD) or 5+ (MS) years of experience outside of education
Very nice to have:
- Past experience working with business metrics and interacting with business/non-technical stakeholders
- Preference for, and experience with, building interpretable, explainable models that deliver insight to end users
- Experience with full-stack machine learning development - from data intake and processing to model development to testing and validation in production
- Structured approach to research and development - willingness to get to a workable implementation quickly today while building towards a more comprehensive solution in the future