Tentek, Inc.
Data Scientist (AdTech/Pricing Optimization)
Tentek, Inc., Los Angeles, California, United States,
Data Scientist (AdTech/Pricing Optimization) Architect - Data/DB New York , California Direct Hire Jan 25, 2024
Data Scientist (AdTech/Pricing Optimization)
Major responsibilities include:
Leading customer
discussions for discovery of requirements, definition of problem statement , and recommendation of solutions Leveraging time series
analysis for trend identification, seasonality adjustments, pricing and yield optimization, and ROI forecasting in advertising . Analyzing and translating business needs into long-term solution data models, utilizing code for analyzing data, and
building statistical, optimization, and ML models . Applying combinatorial optimization research & advanced time series
forecasting techniques to build, test, evaluate machine learning models Using combinatorial optimization for building recommendation algorithms Supporting decision-making by developing analytic capabilities, platforms, pipelines, and using them to analyze trends and find root causes of model inaccuracy.
Minimum qualifications:
8+ years of experience working in the Data Science field with Time Series
Forecasting, Pricing Optimization , and Operations Research problems, conducting hands-on data exploration and analysis, building, deploying and evaluating
ML models for production and commercial applications Prior commercial experience working with
Supply Chain, Yield Optimization, Demand Forecasting, especially in translating optimization and scheduling algorithms into tangible actions for creating, distributing, and placing ads. Proven expertise in the areas of Operations Research, Greedy Heuristics, and Combinatorial Optimization techniques related to Integer Programming and Mixed Integer Programming, and the ability to prototype and solve “knapsack” problems effectively . Experience with
optimization libraries such as Gurobi, Pyomo, Pulp, or Cplex . Knowledge of greedy heuristics and their application in rapid decision-making scenarios within advertising. Proficient with
Amazon SageMaker and Retrieval Augmented Generation . Familiarity with
TV/Linear Advertising space, Ad Campaign Managers (Hulu, Facebook,Google, etc), Media Planning, Publishing/M&E domain, measurement providers and alternative currencies in linear and addressable (e.g. Nielsen, Commscore, iSpot, Samba, EDO, Wideorbit, Mediaocean, etc). Experience in
Media Mix Modeling (MMM) and Targeted Advertising . Ability to harness machine learning algorithms, particularly in targeting specific audiences, enhancing the impact of advertising campaigns. Data Visualization: Provide insights: and show patterns, trends, or correlations in a single plot. The ability to produce dynamic and interactive data visualizations is a plus. Experience leading customer discovery, business requirements and solution design discussions, and managing executive, business and technical stakeholder relationships Demonstrated expertise in using
commercial and open-source solvers, advanced time series analysis and forecasting techniques, including deep learning models, decision tree-based models, ARIMA, Prophet, LSTM / CNN / RNN, etc. Proficient in
Python, Scala, SQL, tools for Data analysis (NumPy / SciPy / scikit-learn / pandas / matplotlib),
demonstrating best practices for data coding to ensure consistency and readiness for production deployment. Statistics and Applied Math, Computer Science, Operations Research (OR). English Fluency
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Major responsibilities include:
Leading customer
discussions for discovery of requirements, definition of problem statement , and recommendation of solutions Leveraging time series
analysis for trend identification, seasonality adjustments, pricing and yield optimization, and ROI forecasting in advertising . Analyzing and translating business needs into long-term solution data models, utilizing code for analyzing data, and
building statistical, optimization, and ML models . Applying combinatorial optimization research & advanced time series
forecasting techniques to build, test, evaluate machine learning models Using combinatorial optimization for building recommendation algorithms Supporting decision-making by developing analytic capabilities, platforms, pipelines, and using them to analyze trends and find root causes of model inaccuracy.
Minimum qualifications:
8+ years of experience working in the Data Science field with Time Series
Forecasting, Pricing Optimization , and Operations Research problems, conducting hands-on data exploration and analysis, building, deploying and evaluating
ML models for production and commercial applications Prior commercial experience working with
Supply Chain, Yield Optimization, Demand Forecasting, especially in translating optimization and scheduling algorithms into tangible actions for creating, distributing, and placing ads. Proven expertise in the areas of Operations Research, Greedy Heuristics, and Combinatorial Optimization techniques related to Integer Programming and Mixed Integer Programming, and the ability to prototype and solve “knapsack” problems effectively . Experience with
optimization libraries such as Gurobi, Pyomo, Pulp, or Cplex . Knowledge of greedy heuristics and their application in rapid decision-making scenarios within advertising. Proficient with
Amazon SageMaker and Retrieval Augmented Generation . Familiarity with
TV/Linear Advertising space, Ad Campaign Managers (Hulu, Facebook,Google, etc), Media Planning, Publishing/M&E domain, measurement providers and alternative currencies in linear and addressable (e.g. Nielsen, Commscore, iSpot, Samba, EDO, Wideorbit, Mediaocean, etc). Experience in
Media Mix Modeling (MMM) and Targeted Advertising . Ability to harness machine learning algorithms, particularly in targeting specific audiences, enhancing the impact of advertising campaigns. Data Visualization: Provide insights: and show patterns, trends, or correlations in a single plot. The ability to produce dynamic and interactive data visualizations is a plus. Experience leading customer discovery, business requirements and solution design discussions, and managing executive, business and technical stakeholder relationships Demonstrated expertise in using
commercial and open-source solvers, advanced time series analysis and forecasting techniques, including deep learning models, decision tree-based models, ARIMA, Prophet, LSTM / CNN / RNN, etc. Proficient in
Python, Scala, SQL, tools for Data analysis (NumPy / SciPy / scikit-learn / pandas / matplotlib),
demonstrating best practices for data coding to ensure consistency and readiness for production deployment. Statistics and Applied Math, Computer Science, Operations Research (OR). English Fluency
#J-18808-Ljbffr