About this vacancy

• Strategic Leadership in Time-Series Analysis: Develop and execute a strategic roadmap for time-series data science initiatives, positioning TMC as a leader in predictive modeling. Identify high-impact opportunities for time-series applications, particularly in sectors where predictive accuracy is critical.
• End-to-End Time-Series Model Development: Lead the design, development, and optimization of time-series models using LSTM networks and other advanced methodologies, ensuring solutions are robust, scalable, and aligned with client objectives.
• Advanced Model Deployment and Governance: Oversee model validation, deployment, and continuous optimization processes, emphasizing long-term performance and reliability. Implement governance frameworks to ensure models comply with data handling and regulatory standards.
• Cross-Functional Collaboration and Strategy Alignment: Collaborate with data engineering, analytics, and business teams to align time-series initiatives with strategic goals. Act as a key interface between technical and non-technical stakeholders to drive understanding and adoption of predictive insights.
• Technical Cloud and Infrastructure Oversight: Guide the utilization of cloud platforms (particularly Azure) and data pipelines optimized for large-scale time-series data processing. Ensure infrastructure is scalable and efficient to support the deployment of LSTM and other models in production.
• Team Leadership and Mentorship: Provide technical mentorship, especially in time-series analysis and neural networks, to junior team members. Lead by example in best practices for feature engineering, hyperparameter tuning, and model monitoring in time-series contexts.
• Stakeholder Engagement and Insight Communication: Regularly engage with business leaders to translate their requirements into actionable data science projects, particularly those centered on time-series forecasting for trend analysis, anomaly detection, and predictive maintenance.

What we expect from you

• Deep Expertise in Time-Series Modeling: Proven experience in time-series forecasting, especially using LSTM networks, ARIMA models, and other advanced techniques. Ability to handle large datasets and deploy real-time predictive models.
• Cloud Proficiency for Time-Series Data: Hands-on experience with Azure services (e.g., Azure ML, Databricks) to build, scale, and deploy time-series models. Familiarity with tools and architectures suited for handling continuous and high-frequency time-series data.
• Advanced Data Science Techniques: Strong grounding in data processing for time-series data, including feature extraction, windowing, and seasonality adjustments. Expertise in model validation and hyperparameter tuning for recurrent neural networks.
• Experience with MLOps for Time-Series: Proficiency in Dev Ops/MLOps, with a focus on automated pipelines for deploying, monitoring, and retraining time-series models. Knowledge of CI/CD for LSTM model deployment.
• Leadership and Mentorship in Time-Series: 5+ years in data science, with demonstrated experience in developing time-series models and leading data science teams.
• Strategic Vision and Stakeholder Communication: Ability to explain time-series modeling techniques and results to non-technical stakeholders, translating technical work into actionable business insights.

What you can expect from us