Data Science Manager
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Job Location:
Dunwoody, GA - USA
Monthly Salary:
Not Disclosed
Posted on:
30+ days ago
Vacancies:
1 Vacancy
Job Summary
- Dunwoody
- Remote
Job Description
We are seeking a Data Science Manager to lead a team of data scientists building production-grade models and data products that drive real business outcomes. This role blends technical leadership hands-on data science and cross-functional collaboration with a strong emphasis on scalable data pipelines advanced modeling for time-based data and applied optimization problems.
You will work closely with Sales Product Owners Engineering and Customers to translate business needs into well-defined technical solutions and guide models from experimentation through deployment in a cloud-native environment.
Key Responsibilities
Leadership & Collaboration
- Lead mentor and grow a team of data scientists setting technical direction and best practices
- Partner with Sales Product Owners and Customers to translate business requirements into actionable analytical and modeling tasks
- Communicate complex analytical concepts clearly to technical and non-technical stakeholders
- Drive prioritization and roadmap planning for data science initiatives
Data Engineering & Pipelines
- Design and oversee scalable data pipelines using PySpark and Databricks
- Ensure data quality reliability and performance across batch and streaming workloads
- Collaborate with data engineering and platform teams to operationalize models
Modeling & Analytics
- Build and review models for sequence and time-based data including forecasting anomaly detection and temporal pattern recognition
- Apply and guide best practices in feature engineering model validation and performance monitoring
- Lead experimentation and iteration to improve model accuracy and business impact
- Ability to perform advanced statistical analysis and modeling such as liner and non-liner regression sampling and Markov chains
Optimization & Applied Algorithms
- Apply operations research and optimization techniques to real-world problems including:
- Last-mile delivery and routing
- Knapsack and resource allocation problems
- Graph-based problems (graph coloring max flow network optimization)
- Translate optimization outputs into actionable recommendations for business teams
Machine Learning Models & Techniques
- Design develop and evaluate a wide range of machine learning models including:
- Classical models (linear/logistic regression tree based models gradient boosting)
- Deep learning models for sequence and temporal data (e.g. temporal convolutional networks RNNs and transformer-based approaches)
- Probabilistic and statistical models for forecasting and uncertainty estimation
- Apply techniques such as feature engineering hyperparameter tuning model selection and cross validation at scale
- Implement anomaly detection causal analysis and signal extraction for operational and telemetry data
- Balance model accuracy interpretability performance and cost in production environments
- Integrate machine learning outputs with optimization and decision support systems
Cloud & Production Deployment
- Work within Azure to deploy and maintain data science solutions
- Leverage Azure Functions and Azure Container Apps for scalable production-grade model serving and workflows
- Ensure models are observable maintainable and cost-efficient in production
Required Experience:
Manager
Key Skills
About Company
About the company MTech Systems helps the food production industry increase yield, improve animal welfare and achieve sustainability. Over 150 leading animal-protein producers rely on our secure cloud-based platform to keep comprehensive information across their operations and supply ... View more
