Data Scientist

Data scientists at Flock Freight work in close conjunction with the broader engineering organization to prototype and operationalize models in experimental and incremental deployments. A successful candidate is trusted by engineers and cross-functional stakeholders alike, capable of tackling complex challenges head-on to address critical business needs. They enjoy exploring our data, engineering new features, and conducting computational experiments to prototype and incrementally develop models. They are a self-starter who takes ownership of their data science projects and is able to go from concept to production in an efficient and reliable manner. 

Outcomes

• Guide first model to production within 6 months of start
• Quantifiably estimate level-of-effort and business impact of new data science projects
• Work closely with stakeholders to obtain buy-in and develop trust in systems
• Design, baseline, and monitor relevant KPIs and performance metrics
• Provide stakeholders (both inside and outside of engineering) persistent visibility into health of production pipelines

Competencies

• Naturally curious to learn how and why things work/behave the way they do
• Ability to quickly learn and understand business concepts and problems
• Ability to champion data science projects and estimate level of effort realistically
• Ability to brainstorm and identify potential solution ideas to stakeholder issues
• Detail oriented with an ability to dive deep into business processes, ability to scope data collection, and an ability to investigate data discrepancies 
• Data obsessed, with the ability to critically assess both quality and availability
• Strong command of statistics and probability
• Ability to develop robust ML models that perform as expected in production
• Ability to communicate complex technical concepts to business stakeholders in an accessible way (e.g. through use of visualizations, and audience-appropriate presentations)
• Comfortable working incrementally to release systems initially as MVP and iterate to improve 
• Ability to incorporate constructive feedback from stakeholders to improve models 
• Thorough -- From consistently and iteratively seeking feedback, to exploring, prototyping, training, testing, and validating new models, the candidate leaves no stone unturned

Knowledge / Experience

• Masters or PhD in Data Science, Computer Science, Statistics, Applied Mathematics, Operations Research, Physical, Life or Social Sciences, Engineering, or related, highly quantitative discipline
• 1-5 years working on data science and machine learning projects, with models successfully transitioned into production
• Mastery of Python and common DS libraries (e.g. SciKitLearn, Scipy, Numpy, TF/PyTorch)
• Mastery of SQL
• Experience in at least one of the following areas: time-series modeling, dynamic pricing systems, marketplaces, recommenders systems, causal inference, experimentation, feature stores, NLP, and LLMs.
• Knowledge with data sampling and splitting techniques
• Knowledge of machine learning algorithms

The expected compensation range for this position is $135,000- $158,000 per year. When determining an offer, we take into account a variety of factors that are important in making compensation decisions including but not limited to skill sets; experience and tenure; education and certifications; and other business and organizational needs. The disclosed range estimate is based on market data for the greater San Diego area. Salary bands may, in some circumstances, be adjusted to a different geographic area depending on the candidate’s position and location. In general, Flock Freight targets the mid-point of this salary range.

In addition to salary, full-time employees are also eligible for an equity package and our competitive benefits that support you and your family as part of your total rewards package at Flock Freight. If you are local to San Diego, Chicago, or the surrounding areas, your presence will be expected in one of Flock Freight’s headquarters locations (San Diego or Chicago) in a hybrid capacity. Ask your recruiter for more specifics about your team's hybrid policy!