Job Description
**Operations Research Data Scientis Job Description**
**Required Technologies**:
**-Python**
**-numpy**
**-SciPy**
**-Scikit learn**
**-pandas**
**-Matplotlib**
**-amazon**
**-data**
**-Data Science**
**-Scala**
**Description**
The Intelligence Unit of the Business Operations team seeks a Data Scientist with strong analytical and communication skills to join our team. We develop algorithms that involve learning from core systems, such as HCM, ERP, ATS, CRM, internal products, and Staffing Systems, in order to generate and improve existing data models.
8+ years of experience working in the Data Science field with Optimization and Time
Series Forecasting problems, preferably leading customer discovery and requirements
discussions, and building/deploying ML models to production.
- Demonstrated 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.
- Proficient in Python/Scala and SQL, demonstrating best practices for data coding to ensure consistency and readiness for production deployment.
- Demonstrated expertise in using commercial and open source solvers, time series analysis, and forecasting techniques.
- Statistics and Applied Math, Computer Science, Operations Research (OR).
- English Proficiency
- Data-focused applied mathematics (statistical analysis)
Preferred Qualifications:
- Python tools for Data analysis (NumPy / SciPy / scikit-learn / pandas / matplotlib).
- 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.
- Prior experience working with Supply Chain, especially in translating optimization and scheduling algorithms into tangible actions for creating, distributing, and placing ads.
- Data Visualization: Provide insights: and show patterns, trends, or correlations in a singl plot. The ability to produce dynamic and interactive data visualizations is a plus.
IMPORTANT!
**Scorecard / Elements evaluated**
**Operations Research**
- Must-Have:_
- Combinatorial Optimization
- Greedy Heuristics
- Mixed Integer Programming
- Commercial & Open Source Solvers
- Nice-to-Have:
- Cardinality Algorithms (e.g. HyperLogLog)
**AI/ML**:
- Must-Have:
- Time Series Modeling
- Forecasting Techniques
- Nice-to-Have:
- Ensemble Trees Techniques
- Neural Network Techniques
- Retrieval Augmented Generation
- MLOps
**Programming**:
- Must-Have:
- Python (OOP, decorators, testing, best practices)
- Scala
- Data Manipulation (deduplication, hashing, feature engineering)
- Nice-to-Have:
- Spark (Processing and ML)
**AWS**:
- Nice-to-Have:
- Sagemaker
- Bedrock
**Soft Skills**:
- Must-Have:
- Technical Leadership
- Drive technical discussions
- Scope complex requirements
- Inquisitive nature
- Clear communication (STAR Method)
**Responsibilities**
Major responsibilities include:
- Analyzing and translating business needs into long-term solution data models, utilizing
code for analyzing data, and building statistical, optimization, and ML models.
- Use combinatorial optimization for media mix modeling and resource allocation.
- Evaluating implemented data systems for variances, discrepancies, and efficiency.
- Leveraging time series analysis for trend identification, seasonality adjustments, and ROI
forecasting in advertising campaigns.
- Supporting decision-making by developing analytic capabilities, platforms, pipelines, and
using them to analyze trends and find root causes of model inaccuracy
Tipo de puesto: Tiempo completo
Salario: $70,000.00 - $75,000.00 al mes
Horario:
- Turno de 8 horas
Prestaciones:
- Seguro de vida
Tipos de compensaciones:
- Bono anual
Experiência:
- Análisis de datos: 6 años (Deseable)
Idioma:
- Inglés avanzado (Obligatorio)
Lugar de trabajo: Empleo remoto
**Required Technologies**:
**-Python**
**-numpy**
**-SciPy**
**-Scikit learn**
**-pandas**
**-Matplotlib**
**-amazon**
**-data**
**-Data Science**
**-Scala**
**Description**
The Intelligence Unit of the Business Operations team seeks a Data Scientist with strong analytical and communication skills to join our team. We develop algorithms that involve learning from core systems, such as HCM, ERP, ATS, CRM, internal products, and Staffing Systems, in order to generate and improve existing data models.
8+ years of experience working in the Data Science field with Optimization and Time
Series Forecasting problems, preferably leading customer discovery and requirements
discussions, and building/deploying ML models to production.
- Demonstrated 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.
- Proficient in Python/Scala and SQL, demonstrating best practices for data coding to ensure consistency and readiness for production deployment.
- Demonstrated expertise in using commercial and open source solvers, time series analysis, and forecasting techniques.
- Statistics and Applied Math, Computer Science, Operations Research (OR).
- English Proficiency
- Data-focused applied mathematics (statistical analysis)
Preferred Qualifications:
- Python tools for Data analysis (NumPy / SciPy / scikit-learn / pandas / matplotlib).
- 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.
- Prior experience working with Supply Chain, especially in translating optimization and scheduling algorithms into tangible actions for creating, distributing, and placing ads.
- Data Visualization: Provide insights: and show patterns, trends, or correlations in a singl plot. The ability to produce dynamic and interactive data visualizations is a plus.
IMPORTANT!
**Scorecard / Elements evaluated**
**Operations Research**
- Must-Have:_
- Combinatorial Optimization
- Greedy Heuristics
- Mixed Integer Programming
- Commercial & Open Source Solvers
- Nice-to-Have:
- Cardinality Algorithms (e.g. HyperLogLog)
**AI/ML**:
- Must-Have:
- Time Series Modeling
- Forecasting Techniques
- Nice-to-Have:
- Ensemble Trees Techniques
- Neural Network Techniques
- Retrieval Augmented Generation
- MLOps
**Programming**:
- Must-Have:
- Python (OOP, decorators, testing, best practices)
- Scala
- Data Manipulation (deduplication, hashing, feature engineering)
- Nice-to-Have:
- Spark (Processing and ML)
**AWS**:
- Nice-to-Have:
- Sagemaker
- Bedrock
**Soft Skills**:
- Must-Have:
- Technical Leadership
- Drive technical discussions
- Scope complex requirements
- Inquisitive nature
- Clear communication (STAR Method)
**Responsibilities**
Major responsibilities include:
- Analyzing and translating business needs into long-term solution data models, utilizing
code for analyzing data, and building statistical, optimization, and ML models.
- Use combinatorial optimization for media mix modeling and resource allocation.
- Evaluating implemented data systems for variances, discrepancies, and efficiency.
- Leveraging time series analysis for trend identification, seasonality adjustments, and ROI
forecasting in advertising campaigns.
- Supporting decision-making by developing analytic capabilities, platforms, pipelines, and
using them to analyze trends and find root causes of model inaccuracy
Tipo de puesto: Tiempo completo
Salario: $70,000.00 - $75,000.00 al mes
Horario:
- Turno de 8 horas
Prestaciones:
- Seguro de vida
Tipos de compensaciones:
- Bono anual
Experiência:
- Análisis de datos: 6 años (Deseable)
Idioma:
- Inglés avanzado (Obligatorio)
Lugar de trabajo: Empleo remoto
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