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Predictive Systems Designer
2 weeks ago
About the Role:
We are seeking an experienced and creative Predictive Systems Designer to architect and develop advanced predictive analytics and AI-driven solutions that power smarter business decisions. You will design systems that leverage machine learning, data modeling, and simulation to forecast outcomes, detect patterns, and enable proactive strategies across our organization.
Key Responsibilities:
Predictive System Design & Architecture
- Design and implement end-to-end predictive systems, including data pipelines, modeling workflows, and inference engines.
- Develop predictive models for forecasting, anomaly detection, optimization, and behavioral prediction across business domains.
- Translate business requirements into scalable, data-driven solutions using advanced statistical and machine learning techniques.
- Architect real-time and batch prediction frameworks that integrate with existing enterprise systems or digital products.
- Evaluate and optimize predictive pipelines for accuracy, latency, and cost efficiency.
Modeling & Algorithm Development
- Build, test, and deploy machine learning and time-series models using Python, R, or other analytics tools.
- Utilize modern ML frameworks such as TensorFlow, PyTorch, Scikit-learn, XGBoost, or Prophet.
- Design automated feature engineering, data quality checks, and model retraining processes.
Systems Integration & Deployment
- Work closely with software engineers and MLOps teams to deploy predictive systems in production environments.
- Integrate models with business applications, APIs, and visualization tools (e.g., Power BI, Tableau, Dash, Streamlit).
- Design and implement feedback loops for continuous learning and performance monitoring.
Research & Innovation
- Stay current with advances in predictive modeling, AI-driven automation, and probabilistic systems.
- Experiment with reinforcement learning, generative modeling, and hybrid simulation methods to improve system adaptability.
- Drive proof-of-concept projects that demonstrate new predictive capabilities or insights.
Preferred Qualifications:
- Experience with real-time prediction systems or streaming architectures (Kafka, Flink, Spark Streaming).
- Knowledge of Bayesian modeling, simulation-based design, or reinforcement learning.
- Familiarity with MLOps tools and frameworks (MLflow, Kubeflow, Vertex Pipelines).
- Background in predictive maintenance, customer analytics, risk forecasting, or supply chain optimization.
