Data Scientist

1 month ago


Los Angeles, United States Triunity Software Full time

Data Scientist (NYC, LA, SF or Seattle)

Onsite Hybrid role - 3 days a week
- Algorithm Development and Maintenance:

- Utilize cutting-edge machine learning techniques to develop and enhance algorithms for personalization, recommendation, and predictive systems.
- Take ownership of maintaining and optimizing algorithms deployed in production environments.
- Serve as the point person for explaining methodologies to both technical and non-technical teams, fostering clear communication.
- Analysis and Algorithm Optimization:

- Conduct in-depth analysis of user interactions within our apps and user profiles to drive improvements in key personalization metrics.
- Collaborate with data scientists and engineers to refine algorithms and enhance their performance continually.
- MVP Development:

- Innovate and develop machine learning products that can be used for new production features or by downstream production algorithms.
- Work closely with cross-functional teams to prototype and operationalize personalization solutions.
- Development Best Practices:

- Maintain and establish best practices for algorithm development, testing, and deployment, ensuring high-quality code and efficient processes.
- Collaboration with Product and Business Stakeholders:

- Identify and define new personalization opportunities by collaborating with product and business stakeholders.
- Collaborate with other data teams to improve data collection, experimentation, and analysis methods.

Required Qualifications:

- 7+ years of analytical experience
- 5+ years of experience developing machine learning models and performing data analysis with Python and tensor-based model development frameworks (e.g. PyTorch, Tensorflow)
- 5+ years writing production-level, scalable code (e.g. Python, Scala)
- 5+ years of experience developing algorithms for deployment to production systems
- In-depth understanding of modern machine learning (e.g. deep learning methods), models, and their mathematical underpinnings for recommendation engines
- In-depth understanding of the latest in natural language processing techniques and contextualized word embedding models
- Experience deploying and maintaining pipelines (AWS, Docker, Airflow) and in engineering big-data solutions using technologies like Databricks, S3, and Spark
- Familiarity with data exploration and data visualization tools like Tableau, Looker, etc.
- Understanding of statistical concepts (e.g., hypothesis testing, regression analysis)
- Ability to gauge the complexity of machine learning problems and a willingness to execute simple approaches for quick, effective solutions as appropriate
- Strong written and verbal communication skills
- Ability to explain how models are used and algorithms behave to both technical and non-technical audiences

Additional Preferred Qualifications:

- MS or PhD in computer science, data science, statistics, math, or related quantitative field
- Production experience with developing content recommendation algorithms at scale
- Experience building and deploying full stack ML pipelines: data extraction, data mining, model training, feature development, testing, and deployment
- Experience with graph-based data workflows such as Apache Airflow
- Experience engineering big-data solutions using technologies like EMR, S3, Spark, Databricks
- Familiar with metadata management, data lineage, and principles of data governance
- Experience loading and querying cloud-hosted databases such as Snowflake
- Familiarity with automated deployment, AWS infrastructure, Docker or similar containers

Flexible work from home options available.


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