Alloy is looking to solve the identity risk problem for companies that offer financial products by enabling them to outpace fraud and confidently serve more people around the world.
Requirements
- Advanced proficiency in scripting languages like Python and querying languages like SQL
- Experience with classification, clustering, regression, and time series models.
- Experience working with unbalanced data sets and regularization methods.
- Experience building models from scratch, iterating, and owning projects end to end.
- Experience maintaining production machine learning models
- Experience with AWS SageMaker
- Airflow, Spark, Dbt, Git, Hex
Responsibilities
- Apply statistical and machine learning methods to build customer-facing models.
- Work closely with application engineers to operationalize models, ensuring they meet rigors for customer usage, including model performance tracking and having mechanisms to retrain models.
- Devise optimization models to recommend ways to improve fraud and compliance workflows.
- Use heuristics, anomaly detection methods, and unsupervised machine learning methods to detect and predict fraud.
- Conduct bespoke analyses and research for new customer use cases that support future development of data science products
- Partner with engineering and product leads to provide guidance and leadership in roadmap planning.
- Thought leadership around data governance and standardization
Other
- Always building with end-solution in mind.
- Able to communicate complicated concepts to a non-technical audience without diluting the complexity of the work.
- Able to build strong cross-functional relationships within Alloy.
- Naturally curious with a knack for asking tough questions.
- A team player. You believe that big things happen when the right people are working together.
- A fast learner
- Humble. Mistakes happen and owning them helps us learn and move on quickly
- An excellent teammate, willing to offer help and advice when needed
- Product-oriented. You have a desire to understand Alloy's business, strategy and priorities to help guide future product development
- BA in a quantitative field, or equivalent experience
- 6 years of relevant experience doing modeling and data science work, conducting advanced analytics and building/iterating on real-world production end-to-end models.
- 2 years experience as a tech lead
- Unlimited PTO and flexible work policy
- Employee stock options
- Medical, dental, vision plans with HSA (monthly employer contribution) and FSA options
- 401k with 100% match up to 4% of annual employee compensation
- Eligible new parents receive 16 weeks of paid parental leave
- Home office stipend for new employees
- Annual Learning & Development annual stipend
- Well-being benefits include access to ClassPass, OneMedical, and Spring Health
- Hybrid work environment: our employees local to NYC are expected to work Tuesdays and Thursdays from our HQ in Union Square, Manhattan.
