Data Scientist - AI & Anomaly Insights

Socket.dev; New York (NY); On-site; USD 153,000 - 208,000; Full time

Benefits offered by this job

Health insurance

401(k) matching

Paid time off

Job summary

Socket.dev is building a cutting-edge AI-driven data platform in the cloud. The Data Scientist will analyze large datasets to uncover idiosyncrasies, outliers, and anomalies that impact model performance across AWS services.

You will translate business problems into data science solutions with high impact in a fast-evolving environment. You will collaborate with senior leaders to trace decisions from raw data to business metrics, contribute to model explainability, and help shape the next

Qualifications

• 2+ years of data scientist experience.
• 3+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience.
• 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience.
• Experience applying theoretical models in an applied environment.

Responsibilities

• Categorizing Customer Idiosyncrasies: summarize differences to help model outcomes.
• Detecting and Cleaning Up Outliers: develop mechanisms to clean up outliers for downstream consumption.
• Deep Diving Customer Issues: analyze discrepancies to determine model vs. customer expectations.
• Assessing Data Gaps: identify derivable features and assess value of new data streams.

Skills

SQL

Python

R

Matlab

ML modeling

Data analysis

Job description

Socket.dev is building a cutting-edge AI-driven data platform in the cloud. The Data Scientist will analyze large datasets to uncover idiosyncrasies, outliers, and anomalies that impact model performance across AWS services.

You will translate business problems into data science solutions with high impact in a fast-evolving environment. You will collaborate with senior leaders to trace decisions from raw data to business metrics, contribute to model explainability, and help shape the next