Meet our team
Dr Abishek Sriramulu
Founder and Chief Technology Officer
Prior to founding Tymestack, Dr Sriramulu worked & collaborated on solving pricing and forecasting problems with several large retailers in the USA, Asia, and Australia.
Dr Sriramulu has a PhD in Machine Learning (Forecasting), was a researcher at Monash University, known for research in time series analytics such as NeuralProphet, Forecast R package, TS-Chief, and ROCKET.
Dr Sriramulu's expertise includes Context Modelling, Graph AI, Multimodal AI, Hierarchical Forecasting.
Dr Christoph Bergmeir
Co-Founder & Chief Forecasting Science Officer
Dr Bergmeir is a Senior Fellow in Computer Science and AI at University of Granada, and Adjunct Senior Research Fellow in Data Science & AI at Monash University.
Dr Bergmeir has worked and collaborated with several large organisations such as Meta USA, Walmart, Tokopedia, Honeywell Ltd, Worley, Yarra Valley Water, and Catch.com.au.
With over 9,180 citations and an h-index of 36, his publications on time series forecasting have been Clarivate Web of Science Highly Cited Papers (top 1% in forecasting).
Dr Bergmeir was involved in several of widely-used open-source initiatives including Forecastingdata.org, Neural Prophet, KATS, and Forecast R Package.
His expertise is focused on ML Forecasting, explainable AI local interpretability, capacity planning, sustainable energy forecast + optimize, and supply chain optimization.
Dr Peter Stuckey
Optimization Advisor
Peter J. Stuckey is a Professor in the Faculty of Information Technology at Monash University, and project leader in the Data61 CSIRO laboratory.
Prof. Stuckey is a pioneer in constraint programming, the science of modeling and solving complex combinatorial problems.
With a Google Scholar h-index of 69, his research interests include: discrete optimization; programming languages, in particular declarative programming languages; constraint solving algorithms; bio-informatics; and constraint-based graphics. He enjoys problem solving in any area, having publications in e.g. databases, timetabling, and system security, and working with companies such as Oracle and Rio Tinto. Prof. Stuckey received a B.Sc and Ph.D both in Computer Science from Monash University in 1985 and 1988 respectively.
In 2009 he was recognized as an ACM Distinguished Scientist. In 2010 he was awarded the Google Australia Eureka Prize for Innovation in Computer Science for his work on lazy clause generation. He was awarded the 2010 University of Melbourne Woodward Medal for most outstanding publication in Science and Technology across the university. In 2019 he was elected as a Fellow of the Association for the Advancement of Artificial Intelligence.
Dr Wray Buntine
Predictive Modelling & AI Advisor
Professor Wray Buntine is Australia’s foremost scholar for the statistical analysis of text and related structured content and their predictive modelling. His global reputation was built over several decades of state of the art machine learning, generative AI and large language model research.
Professor Buntine has authored 18 book chapters, 48 journal articles, and 81 refereed conference papers. His work includes several software products and two patents, with over 13,000 citations and a Google Scholar h-index of 51, reflecting his significant impact on the fields of data science and machine learning.
Professor Buntine is currently Director of the Computer Science Program at VinUniversity. He is set to serve as the General Chair for the 2024 Asian Conference on Machine Learning in Hanoi. He co-edits the ACM Transactions on Probabilistic Machine Learning and serves on editorial boards for several other journals. He has worked on projects for the Helsinki Institute for Information Technology, NASA Ames Research Center, University of California, Berkeley, and Google. His active involvement in the academic community includes senior program committee roles at top conferences like IJCAI, UAI, AAAI, EMNLP, ICLR, ACML, and NeurIPS. Previously, he was the founding director of the Master of Data Science program at Monash University, where he also directed the Machine Learning Group.
With respect to large language models, he has published with Dr. Ehsan Shareghi of Monash University on use of LLMs in contexts requiring uncertainty or the resort to external knowledge resources. At VinUniversity he works with Dr. Le Duy Dung on recommender systems and has published with Dr. Nguyen Quoc Dat of VinAI, co-developer of PhoGPT, and students on the application of LLMs for a variety of applications including healthcare. He also co-developed the MTP multimedia dataset published at ACL 2024 and is a member of Meta’s Open Innovation AI Research Community, where he works with a small community of scientists furthering educational applications of Generative AI and Llama in particular.
Dr Charles Ng
Optimization Advisor
Dr Ng is an experienced Data Science leader with a successful track record of building solutions based on forecasting, optimization, and machine learning techniques to tackle complex real-world problems.
He has a PhD in Mgmt Science & Engineering from Stanford University, as well as a BS in Actuarial Science & Mathematics from Purdue University.
Dr Ng was previously the Director Data Science, Meta USA. Dr Ng also held senior roles at Appier, Coupang, and DemandTec.
Dr Nicolas Fourrier
Co-Founder & AI Advisor
Dr Fourrier is a leading practioner in the application of forecasting methodologies to ML and AI including agentic systems.
Dr Fourrier has a PhD and MSc in Mathematics from the University of Virginia, as well as a M.Applied Mathematics from ESILV in Paris.
His research specialities include AI, agentic platforms, zero-knowledge proofs, ML in retail, assortment optimization, planning and pricing, differential equations, dynamic systems, and predictive modelling of complex systems.
Rachel Gabato
Retail Advisor
Rachel is a veteran retail advisor with specific experience in the growth of retail technology startups.
Rachel is the Senior Director, Product Mgmt Merchandising Innovation Technologies, at Walmart USA. And was previously Product Mgmt Advisor, Walmart.
Heavily involved with Walmart Ventures she has focused on building digital merchants utilizing ML and AI.
Rachel has a BS, Applied Economics & Business Mgmt from Cornell University.
Theunis Groenewald
Co-Founder & UX/UI Designer
Theunis is an experienced UX/UI designer across retail environments—both brick-and-mortar and e-commerce.
He is an accomplished international brand designer, developer, digital and UX design for desktop, mobile and backend systems. Motion graphics and 3D rendering are also in his core skillset.
Born and bred in South Africa, Theunis has recently migrated to New Zealand.
Paul Shale
Co-Founder & Chief Executive Officer
Paul is an experienced growth marketer with a focus on scaling technology startups, retail, data, and advanced technologies.
He was previously CCO at Nextspace (digital twins), CEO Roadtrippers Australasia (tourism data). He was CEO of FCB NZ, and Founder of Consortium, in turn founding several companies including Red Antler NYC.
Paul has decades of retailer / retail brands revenue growth experience in the USA and Australasia: working with Walmart, Sara Lee, Danone, Nestle, Clorox, Goodman-Fielder, Hanes, McDonald's, NZLC, and Foodstuffs NZ.
Paul was previously a commercial lawyer (Russell McVeagh) as well as an accountant specializing in company valuations and tax (Deloitte).
Paul has a B.Com / LLB (Hons) from Auckland University, New Zealand.
Join our team
Data Analyst & Engineer
About Tymestack
Tymestack is a well-funded AI startup that offers advanced forecasting and optimization solutions for retailers. Our platform helps businesses make data-driven decisions to improve inventory management, demand planning, and operational efficiency. We are looking for a Data Analyst & Engineer with expertise in building data pipelines and creating insightful dashboards using Google Cloud to help us deliver actionable insights to our clients.
About the role
As a Data Analyst & Engineer at Tymestack, you will be responsible for managing, transforming, and visualizing data to provide valuable insights for our clients. You will collaborate closely with cross-functional teams, including data scientists, product managers, and developers, to build and maintain dashboards and reporting tools that empower our clients to make data-driven decisions.
Responsibilities
- Design, develop, and maintain dashboards and visualizations using Google Cloud tools such as BigQuery, Looker, and Data Studio.
- Build and manage data pipelines for collecting, transforming, and analyzing large-scale datasets using tools like Google Cloud Dataflow.
- Collaborate with data scientists and stakeholders to define data requirements and ensure dashboards provide actionable insights based on forecast and optimization models.
- Ensure data integrity and accuracy by implementing data validation and monitoring processes across all dashboards.
- Continuously optimize dashboard performance to ensure scalability, fast query response times, and ease of use.
- Implement ETL/ELT processes to extract, transform, and load data into appropriate formats for analysis and visualization.
- Work with cross-functional teams to understand business goals and translate them into data and reporting solutions.
- Stay up-to-date with the latest trends in data visualization and Google Cloud services to improve the overall data architecture and dashboard experience.
Requirements
- Proven experience (3+ years) as a Data Analyst or Data Engineer, with hands-on experience in building dashboards and visualizing data.
- Knowledge of Python for data manipulation and analysis.
- Expertise in Google Cloud tools such as BigQuery, Looker, and Data Studio for building and managing scalable dashboards.
- Proficiency in SQL and experience with data modeling and querying large datasets.
- Experience in building and optimizing ETL/ELT pipelines using Dataflow, Cloud Functions, or similar tools.
- Strong understanding of data visualization principles and the ability to turn raw data into meaningful insights through clear, actionable dashboards.
- Excellent analytical and problem-solving skills with a focus on data accuracy, integrity, and performance.
- Strong communication skills and the ability to collaborate with cross-functional teams to understand business needs and deliver effective data solutions.
Bonus capabilities
- Experience working in retail or e-commerce industries, with an understanding of inventory management, demand forecasting, or supply chain optimization.
- Familiarity with machine learning models and integrating model outputs into dashboards.
- Hands-on experience with Google Cloud Composer or Apache Airflow for data orchestration and automation.
What we offer
- Competitive salary.
- Flexible remote work opportunities with a hybrid option.
- Opportunities for professional development and growth within a fast-paced, innovative company.
- Work with a passionate team committed to revolutionizing forecasting and optimization for retailers using cutting-edge technology.
How to apply
Interested in turning data into actionable insights with Tymestack? Apply now by sending your resume and a brief cover letter to careers@tymestack.com. We encourage candidates from all backgrounds to apply.
Full Stack Developer
About Tymestack
Tymestack is a well-funded AI startup that offers advanced forecasting and optimization solutions for retailers. Our platform helps businesses make data-driven decisions to improve inventory management, demand planning, and operational efficiency. We are looking for a talented Full Stack Developer to join our team and help us build and scale our forecasting and optimization platform.
About the role
As a Full Stack Developer at Tymestack, you will play a key role in transforming design concepts into reality. You will collaborate with our product and design teams to turn Figma design files into responsive, scalable, and efficient full-stack web applications. Your work will ensure seamless integration between frontend and backend systems while providing users with real-time forecasting and optimization insights.
Responsibilities
- Turn design files from Figma into full-stack web applications with a focus on responsiveness, performance, and usability.
- Design, develop, and maintain end-to-end web applications using modern full stack technologies.
- Build highly responsive and intuitive user interfaces for our SaaS platform that deliver real-time insights and analytics to users.
- Develop and optimize backend APIs and services to process large-scale data and power forecasting and optimization models.
- Ensure scalability, reliability, and performance of the platform by implementing best practices in code structure, CI/CD pipelines, and cloud infrastructure.
- Collaborate with data scientists to integrate machine learning models into the platform, providing users with real-time forecasting and optimization tools.
- Implement secure and efficient data handling processes to store and retrieve data from cloud-based databases.
- Develop and maintain RESTful APIs and ensure smooth integration between frontend and backend systems.
- Participate in code reviews, ensure adherence to best coding practices, and contribute to architecture and design discussions.
- Troubleshoot and resolve technical issues, continuously improving the performance and scalability of the platform.
Requirements
- Proven experience (3+ years) as a Full Stack Developer with expertise in both frontend and backend development.
- Strong proficiency in JavaScript/TypeScript and React (or similar frontend frameworks like Vue or Angular) for building responsive web applications.
- Experience in turning Figma designs into fully functioning applications, focusing on UX/UI, responsiveness, and performance.
- Proficiency in backend development using Node.js, Python, or similar languages, with experience building RESTful APIs.
- Experience with databases (SQL or NoSQL), and handling large-scale data operations in a cloud environment (e.g., Google Cloud, AWS).
- Familiarity with cloud-based infrastructure, including CI/CD pipelines, containerization (e.g., Docker), and orchestration (e.g., Kubernetes).
- Strong understanding of version control systems (e.g., Git) and collaborative development practices.
- Experience with frontend and backend testing frameworks to ensure high-quality code (e.g., Jest, Mocha, Cypress).
- Knowledge of SaaS platforms and experience building scalable, cloud-native applications.
- Excellent problem-solving skills and attention to detail, with a strong commitment to writing clean, maintainable code.
- Great communication skills and ability to work collaboratively in cross-functional teams.
Bonus capabilities
- Experience working in the retail or e-commerce industry with a focus on inventory management, demand forecasting, or supply chain optimization.
- Familiarity with machine learning concepts and integrating data science models into applications.
- Hands-on experience with cloud services like Google Cloud Platform (GCP) or AWS, including serverless architectures and cloud-based databases.
- Knowledge of security best practices and data privacy in SaaS applications.
What we offer
- Competitive salary.
- Flexible remote work opportunities with a hybrid option.
- Opportunities for professional development and growth within a fast-paced, innovative company.
- Work with a passionate team committed to revolutionizing forecasting and optimization for retailers using cutting-edge technology.
How to apply
Ready to turn Figma designs into full-stack applications and shape the future of retail optimization? Apply now by sending your resume and a brief cover letter to careers@tymestack.com. We encourage candidates from all backgrounds to apply.
Optimization Expert (Inventory Management)
About Tymestack
Tymestack is a well-funded AI startup that offers advanced forecasting and optimization solutions for retailers. Our platform helps businesses make data-driven decisions to improve inventory management, demand planning, and operational efficiency. We are looking for a skilled Optimization Expert to help us enhance our inventory management solutions by building and optimizing algorithms based on forecasted data.
About the role
As an Optimization Expert at Tymestack, you will work closely with our data science and product teams to develop and implement optimization models that leverage our forecasting platform for improved inventory management. You will design and implement algorithms that ensure optimal inventory levels, balance supply and demand, and minimize costs while avoiding stockouts or overstock situations.
Responsibilities
- Develop and implement optimization algorithms that use forecast data to drive inventory decisions, focusing on minimizing costs and optimizing stock levels.
- Analyze and interpret forecasted demand data to inform decision-making for inventory replenishment and distribution.
- Build models that optimize supply chain and inventory management processes, including procurement, stocking, and warehousing strategies.
- Design inventory policies and strategies to ensure just-in-time (JIT) inventory, reduce holding costs, and minimize stockouts.
- Work closely with data scientists and engineers to integrate optimization algorithms with existing machine learning models and data pipelines.
- Develop simulation tools to test different inventory strategies and evaluate performance under varying demand scenarios.
- Monitor the performance of optimization models and continuously refine them based on new data and business needs.
- Collaborate with cross-functional teams to ensure the optimization models are scalable, reliable, and aligned with business objectives.
- Stay up-to-date with the latest trends and methodologies in optimization, operations research, and supply chain management.
Requirements
- Proven experience (6+ years) in optimization, operations research, or a related field, with a focus on inventory or supply chain management.
- Strong expertise in optimization techniques (e.g., linear programming, mixed-integer programming, constraint programming) and algorithm design.
- Proficiency in working with large-scale datasets and applying mathematical models to solve real-world problems.
- Experience with forecasting models and the integration of forecast data into optimization algorithms.
- Strong proficiency in programming languages such as Python or R, and familiarity with optimization libraries (e.g., PuLP, Gurobi, CPLEX).
- Solid understanding of inventory management principles, supply chain dynamics, and demand forecasting.
- Ability to work with data pipelines and cloud infrastructure (e.g., Google Cloud, AWS) to deploy and maintain optimization solutions.
- Strong problem-solving skills and a proactive approach to troubleshooting and improving optimization models.
- Excellent communication and collaboration skills, with the ability to work cross-functionally.
Bonus capabilities
- Experience in inventory optimization for industries such as retail, manufacturing, or e-commerce.
- Familiarity with forecasting platforms and machine learning models, including time-series analysis and predictive analytics.
- Hands-on experience with cloud-based platforms for optimization (e.g., Google Cloud Platform, AWS).
- Knowledge of statistical and simulation techniques to model and solve complex inventory problems.
What we offer
- Competitive salary.
- Flexible remote work opportunities with a hybrid option.
- Opportunities for professional development and growth within a fast-paced, innovative company.
- Work with a passionate team committed to revolutionizing inventory management through cutting-edge technology.
How to apply
Ready to optimize inventory management with data-driven decisions? Apply now by sending your resume and a brief cover letter to careers@tymestack.com. We encourage candidates from all backgrounds to apply.
MLOps Engineer
About Tymestack
At Tymestack, we're building a next-generation forecasting & optimization platform powered by AI. Our goal is to revolutionize how businesses predict and act on future trends, enabling better decision-making with precise, scalable, and real-time insights. We are seeking a skilled MLOps Engineer to join our team and help us build a robust, automated, and scalable machine learning infrastructure.
About the role
As an MLOps Engineer, you will play a critical role in developing and enhancing our MLOps and CI/CD pipelines for our forecasting products. You will work closely with data scientists and engineers to automate and streamline model training, deployment, and monitoring using Google Cloud Platform (GCP) and Vertex AI. This role requires deep experience in creating scalable CI/CD pipelines, enabling data drift detection, automated retraining, and parallelizing hyperparameter tuning using Kubernetes.
Responsibilities
- Design, implement, and maintain CI/CD/CT pipelines to automate every phase of the machine learning lifecycle from data ingestion, model training, testing, and deployment to production.
- Continuous Integration (CI): Build CI pipelines to automatically test and validate data, code, and model updates. Integrate with source control systems (e.g., Git) to trigger workflows on code changes.
- Continuous Delivery (CD): Automate model deployment to production environments using tools like Google Cloud Build and Vertex AI, ensuring that models are delivered continuously without downtime.
- Continuous Training (CT): Implement data drift detection mechanisms and establish automated model retraining workflows to ensure model accuracy over time. Regularly monitor model performance in production, retrain when needed, and ensure the latest data is incorporated into the model.
- Automated Testing: Implement automated testing frameworks to validate data integrity, model performance, and drift detection at every stage of the pipeline.
- Model Versioning: Establish automated versioning of models and datasets to ensure seamless rollback in case of failures.
- Monitoring and Alerts: Integrate monitoring tools to track pipeline performance, model accuracy, and system health. Implement alerting systems for data drift, failed deployments, and performance bottlenecks.
- Work on Google Cloud services, including Vertex AI, Kubernetes, and other GCP tools to build scalable, reliable infrastructure.
- Parallelize hyperparameter tuning and model training using Kubernetes to optimize computational resources.
- Develop robust infrastructure-as-code solutions using Terraform or similar tools to automate cloud resource provisioning and management.
- Collaborate with cross-functional teams, including data scientists and software engineers, to ensure seamless integration of machine learning models into production environments.
- Maintain the scalability, performance, and reliability of deployed models by implementing auto-scaling mechanisms in Kubernetes clusters.
- Continuously improve the pipelines by adopting new tools and techniques, optimizing workflows for faster deployments and better model performance.
Requirements
- Proven experience (3+ years) in MLOps, DevOps, or related fields, with a focus on automating machine learning workflows.
- Expertise in Google Cloud Platform (GCP), including Vertex AI, Kubernetes, and other key services.
- Hands-on experience designing and managing CI/CD pipelines for machine learning, including testing, versioning, and monitoring.
- Proficiency in scripting and automation (Python, Bash) and experience with infrastructure-as-code tools like Terraform.
- Hands-on experience with containerization and orchestration technologies such as Docker and Kubernetes.
- Strong understanding of data drift detection, automated model retraining, auto-scaling, and hyperparameter tuning in distributed machine learning systems.
- Familiarity with data pipeline tools and orchestration frameworks (e.g., Dataflow).
- Strong problem-solving skills with a proactive approach to troubleshooting and resolving issues.
- Excellent communication skills and a collaborative mindset.
Bonus capabilities
- Experience with other cloud platforms (AWS, Azure).
- Familiarity with ML frameworks such as XGBoost, BigQuery ML, TensorFlow or PyTorch.
- Prior experience in working with time-series data or forecasting models.
What we offer
- Competitive salary.
- Flexible remote work opportunities with a hybrid option.
- Opportunities for professional development and growth within a fast-paced, innovative company.
- Work with a passionate team committed to revolutionizing forecasting with cutting-edge technology.
How to apply
Interested in making an impact at Tymestack? Apply now by sending your resume and a brief cover letter to careers@tymestack.com