Applied Data Scientist

Fingerprint empowers developers to stop online fraud at the source.

We work on turning radical new ideas in the fraud detection space into reality. Our products are developer-focused and our clients range from solo developers to publicly traded companies. We are a globally dispersed, 100% remote company with a strong open-source focus. Our flagship open-source project is FingerprintJS (20K stars on GitHub).

We have raised $77M and are backed by Craft Ventures (previously invested in Tesla, Facebook, Airbnb), Nexus Venture Partners (previously invested in Postman, Apollo.io, MinIO, Druva) and Uncorrelated Ventures (previously invested in Redis, Rollbar & Gradle).

We are seeking an Applied Data Scientist to join our innovative team. In this role, you will leverage machine learning and data engineering techniques to build robust, scalable solutions for fraud detection. You will collaborate closely with engineers to integrate machine learning models into production systems and work on projects that analyze large datasets to derive insights into browser and device behavior.

In addition to your data science expertise, you will be responsible for writing production-quality backend code and owning features from initial concept and research through to final deployment. This includes developing backend components to seamlessly integrate solutions into our systems. The role requires strong hands-on experience in backend development, as well as collaboration with cross-functional teams to deliver fully integrated, end-to-end solutions.

Types of Projects and Impact :

1. Collaborate with the Smart Signals Product team to improve fraud detection signals, including browser bot detection, VM detection, VPN detection, and more.
2. Conduct deep dives into problematic features, researching and analyzing their behavior to understand root causes and identify potential solutions. Develop hypotheses, run experiments, analyze results, and translate findings into actionable engineering improvements.
3. Build and enhance backend systems for real-time data processing and model inference.
4. Develop scalable web services that integrate machine learning models to analyze large datasets and extract insights.
5. Drive the integration of machine learning solutions into production systems, ensuring performance, scalability, and reliability.
6. Foster a data-driven culture by sharing engineering best practices and collaborating on cross-functional projects.

Position Overview : As an Applied Data Scientist , you will be responsible for developing and maintaining backend services that leverage machine learning algorithms for fraud detection. Your role will focus on end-to-end engineering, from building scalable data pipelines to deploying ML models in production environments.

Required Skills :

1. Proficient in English for clear communication in a global, remote team.
2. BS/MS in Computer Science, Data Science, or a related field, or equivalent work experience.
3. 3+ years of experience in backend development with exposure to data science and machine learning.
4. Backend Engineering Expertise :
   
   1. Strong experience in designing, developing, and maintaining scalable backend systems.
   2. Experience working with real-time data processing, APIs, and integrating machine learning models into production services.
   3. Excellent coding skills, particularly in GoLang (or equivalent), with working knowledge of data engineering practices.
5. Machine Learning Knowledge :
   
   1. Familiarity with supervised and unsupervised learning methods.
   2. Experience working with machine learning pipelines, model deployment, and performance monitoring.
   3. Understanding of core ML concepts such as feature engineering, model evaluation, and real-time inference.
6. Strong knowledge of SQL and experience with databases like DynamoDB, Redis, or Elasticsearch.
7. Proficiency with general software engineering tools: Git, IDEs, shell scripting, CI/CD.

Nice to Have :

1. Practical experience with analytical storage systems like ClickHouse, Snowflake, BigQuery, Redshift, or Databricks.
2. Experience with data transformation frameworks like dbt or other data pipeline tools.
3. Familiarity with data visualization tools such as Apache Superset, Tableau, or Looker.
4. Experience with the Python data analytics stack (Numpy, Pandas, Jupyter, etc.).

Technologies You Will Work With :

1. Backend development: GoLang (preferred) or equivalent.
2. ML stack: Flexible, with CatBoost used in production.
3. Data analytics/processing: ClickHouse, dbt, Apache Superset.
4. Infrastructure: AWS, DynamoDB, Redis, Elasticsearch.

Compensation Range :
$130,000 - $190,000
For cash compensation, we set standard ranges for all US based roles based on function, level and geographic location, benchmarked against similar stage growth companies. In order to be compliant with local legislation, as well as to provide greater transparency to candidates, we share salary ranges on all job postings regardless of desired hiring location.

Offers vary depending on, but not limited to, relevant experience, education, certifications/licenses, skills, training, and market conditions.

Due to regulatory and security reasons, there's a small number of countries where we cannot have Fingerprint teammates based. Additionally, because Fingerprint is an all-remote company and people can join our workforce from almost any country, we do not sponsor visas. Fingerprint teammates need to be authorized to work from their home location.

We are dedicated to creating an inclusive work environment for everyone. We embrace and celebrate the unique experiences, perspectives and cultural backgrounds that each employee brings to our workplace. Fingerprint strives to foster an environment where our employees feel respected, valued and empowered, and our team members are at the forefront in helping us promote and sustain an inclusive workplace. We highly encourage people from underrepresented groups in tech to apply.#J-18808-Ljbffr