Engineer III, Machine Learning Software

Samsung Electronics•Mountain View, CA

1d•Hybrid

About The Position

MULTIPLE POSITIONS AVAILABLE Company: SAMSUNG ELECTRONICS AMERICA, INC. Position Title: Engineer III, Machine Learning Software Location: Mountain View, CA Job ID: SAM9441233 Position Responsibilities: Work as a tech lead to deliver end-to-end Machine Learning solutions to effectively drive business and technical initiatives. Design, build, and productionize machine learning models to solve complex problems in areas like ad targeting and performance prediction. Utilize statistical analysis, experimentation (A/B testing), and machine learning to optimize platform performance and drive business growth. Collaborate closely with ML engineers to define, refine, and improve existing machine learning products and features. Take ownership of data science projects from inception to production, ensuring scalability and reliability. Effectively communicate complex technical findings and insights to both technical and non-technical audiences, including leadership, product owners, and engineering teams. Proactively examine business health of and identify product bottlenecks in the entire Samsung Ads ecosystem to unveil new business opportunities, help strategic decision making, and suggest technical solutions that can streamline workflows, improve user experiences, and open new avenues for growth through data analysis. Position Requirements: Ph.D. degree or foreign equivalent degree in Computer Science, Engineering, Mathematics, or a related field and three years of research or work experience in the job offered or as a Research Scientist, Data Scientist, Machine Learning Engineer or another related occupation. Must have three years of research or work experience in the following skills: (1) experience in the full lifecycle of machine learning model development from feature engineering to production deployment; (2) proficiency with all of the following algorithms: (a) DNNs, (b) RNNs, (c) LSTMs, (d) Gradient Boosting, (e) XGBoost, (f) SVMs, (g) VAR, (h) NNAR, and (i) Logistic Regression, and the ability to select the optimal approach for a given problem; (3) programming with Python or Go; (4) deep understanding of data manipulation libraries including (a) Pandas, (b) NumPy, and (c) Scikit-learn, and experience building production code; (5) experience with version control systems including Git and CI/CD pipelines for automated model deployment; (6) experience in data wrangling and integration; (7) experience identifying, accessing, and integrating diverse data sources, including proactively addressing data limitations and extending data applicability; (8) demonstrated experience in data quality assessment and remediation; (9) hands-on experience with Spark and AWS data processing services; (10) experience building and automating ETL pipelines using workflow management tools including Airflow; (11) experience problem framing and solution design including the ability to translate business problems into well-defined data science problems and design innovative solutions. Partial telecommuting permitted; employees will be required to report to office multiple days per week. Hours: Full Time, 40 hours/week. Salary Range: $208,328/year to $240,000/year Position Requirements: Bachelor's degree or foreign equivalent degree in Data Science or a related field and two years of experience in the job offered, or as an Advertising Data Analyst, Advertising Data Engineer, or another related occupation. Must have two years of experience with all of the following skills and tools: (1) TV Data including App Session data, App Usage Data, Video ACR (Automatic Content Recognition), Audio ACR, Tizen Function Data, HDMI Session Data, or User Guide Data; (2) SQL and Python; (3) Data Engineering; (4) Demand-Side Platform and Data Management Platform; (5) Snowflake; (6) Mobile based data including Phone usage sessions, App Usage Sessions, App Install Metadata, Samsung Account Data, or MMP Data; (7) IOT Based data including TV’s, Phones, Appliance Data, or SmartThings data; (8) AWS S3 and AWS EC2; (9) Documentation including Business Requirements Documents (BMD), Product Requirements Documents (PRD), Engineering Requirements Documents (ERD), OpenMetaData, Authorization Documentation, or Release and Version control; (10) 1P and 3P data; (11) Jira and Confluence; and (12) Slack and Salesforce. Partial telecommuting permitted; employees will be required to report to office multiple days per week. Hours: Full Time, 40 hours/week. Salary Range: $120,000/year to $140,000/year #LI-DNI

Requirements

Ph.D. degree or foreign equivalent degree in Computer Science, Engineering, Mathematics, or a related field and three years of research or work experience in the job offered or as a Research Scientist, Data Scientist, Machine Learning Engineer or another related occupation.
Must have three years of research or work experience in the following skills: (1) experience in the full lifecycle of machine learning model development from feature engineering to production deployment; (2) proficiency with all of the following algorithms: (a) DNNs, (b) RNNs, (c) LSTMs, (d) Gradient Boosting, (e) XGBoost, (f) SVMs, (g) VAR, (h) NNAR, and (i) Logistic Regression, and the ability to select the optimal approach for a given problem; (3) programming with Python or Go; (4) deep understanding of data manipulation libraries including (a) Pandas, (b) NumPy, and (c) Scikit-learn, and experience building production code; (5) experience with version control systems including Git and CI/CD pipelines for automated model deployment; (6) experience in data wrangling and integration; (7) experience identifying, accessing, and integrating diverse data sources, including proactively addressing data limitations and extending data applicability; (8) demonstrated experience in data quality assessment and remediation; (9) hands-on experience with Spark and AWS data processing services; (10) experience building and automating ETL pipelines using workflow management tools including Airflow; (11) experience problem framing and solution design including the ability to translate business problems into well-defined data science problems and design innovative solutions.
Bachelor's degree or foreign equivalent degree in Data Science or a related field and two years of experience in the job offered, or as an Advertising Data Analyst, Advertising Data Engineer, or another related occupation.
Must have two years of experience with all of the following skills and tools: (1) TV Data including App Session data, App Usage Data, Video ACR (Automatic Content Recognition), Audio ACR, Tizen Function Data, HDMI Session Data, or User Guide Data; (2) SQL and Python; (3) Data Engineering; (4) Demand-Side Platform and Data Management Platform; (5) Snowflake; (6) Mobile based data including Phone usage sessions, App Usage Sessions, App Install Metadata, Samsung Account Data, or MMP Data; (7) IOT Based data including TV’s, Phones, Appliance Data, or SmartThings data; (8) AWS S3 and AWS EC2; (9) Documentation including Business Requirements Documents (BMD), Product Requirements Documents (PRD), Engineering Requirements Documents (ERD), OpenMetaData, Authorization Documentation, or Release and Version control; (10) 1P and 3P data; (11) Jira and Confluence; and (12) Slack and Salesforce.

Responsibilities

Work as a tech lead to deliver end-to-end Machine Learning solutions to effectively drive business and technical initiatives.
Design, build, and productionize machine learning models to solve complex problems in areas like ad targeting and performance prediction.
Utilize statistical analysis, experimentation (A/B testing), and machine learning to optimize platform performance and drive business growth.
Collaborate closely with ML engineers to define, refine, and improve existing machine learning products and features.
Take ownership of data science projects from inception to production, ensuring scalability and reliability.
Effectively communicate complex technical findings and insights to both technical and non-technical audiences, including leadership, product owners, and engineering teams.
Proactively examine business health of and identify product bottlenecks in the entire Samsung Ads ecosystem to unveil new business opportunities, help strategic decision making, and suggest technical solutions that can streamline workflows, improve user experiences, and open new avenues for growth through data analysis.

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