Position Summary

Role and Responsibilities

Job Description:

Act as data scientist of the Samsung Ads Platform Intelligence (PI) team to leverage unique data to build and deploy impactful machine learning solutions. Focus on applying advanced ML techniques to address key product challenges, identify new revenue opportunities, and optimize performance across the Samsung Ads platform. Responsible for the full ML lifecycle – from problem definition and data exploration to model development, deployment, and monitoring – working closely with a talented team of engineers and researchers. Develop & Deploy ML Models including designing, building, and productionizing machine learning models to solve complex problems in areas like ad targeting and performance prediction. Data-Driven Optimization including utilizing statistical analysis, experimentation (A/B testing), and machine learning to optimize platform performance and drive business growth. ML Product Improvement including collaborating closely with the 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. Data Exploration & Feature Engineering including extracting, cleaning, and transforming unique datasets to create impactful features for machine learning models. Effectively communicate complex technical findings and insights to both technical and non-technical audiences, including leadership, product owners, and engineering teams. Contribute directly to the success of a leading advertising platform.

Minimum Education & Experience Requirements:

Master’s degree in Computer Science, Mathematics, Operations Research, a related field, or a foreign equivalent plus 2 years of post-baccalaureate experience in job offered or any engineering/data scientist related job titles.

Applicants must have at least 2 years of experience in the following: (1) Data Wrangling & Integration including identifying, accessing, and integrating diverse data sources, including addressing data limitations and extending data applicability; (2) statistical theories and methodologies, including hypothesis testing, regression analysis, Bayesian methods, causal inference, time series analysis, and experimental design (A/B testing); (3) Data Modeling including transforming raw data into features and feature engineering techniques, including variable transformation, bucketization, and creation of calculated variables; (4) Machine Learning model developing from feature engineering to production deployment; (5) Machine Learning algorithms (DNNs, RNNs, LSTMs, Gradient Boosting, XGBoost, SVMs, VAR, NNAR, and Logistic Regression); (6) model validation, including bias detection and error analysis; (7) Big Data Technologies including experience with Spark and AWS data processing services; (8) Python or Go; and (9) data manipulation libraries (Pandas, Num Py, Scikit-learn).

The salary range for this role is expected to be between $200,000 – $210,000/Year. Actual pay will be determined considering factors such as relevant skills and experience, and comparison to other employees in the role.

No sponsorship available for this position.

Skills and Qualifications

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