The Product Manager for AI products, manages AllieML which is a traditional machine learning product that predicts the probability of downtime, as well as the potential reason for downtime. They also manage our Generative AI product - Factory GPT - which is a generative business intelligence product that allows you to talk to the factory with natural language, querying factory data to generate charts tables or other output.

Main Responsibilities

1. Understand latent needs and pain points: As is common for a product manager, the core part of the job is being able to talk to users, develop a persona, and understand the user journey and its key pain points. This also involves being able to prioritize pain points based on frequency, and level of pain. 
2. Define predictions for ML: For Allie ML, we have an initial roadmap of predictions (eg., predicting downtime), but you will quickly bring new and innovative ideas of things that might be valuable to predict for customers. This might include specific Factory kpis, or other numbers such as Manufacturing performance, quality, availability or others.
3. Define Generative AI use cases: For FactoryGPT, enhance the future roadmap of what our generative AI product might do for factory heads of quality, managers, and line supervisors. We have a roadmap for the next few months, but it is yours to own and develop.
4. Manage Road map: we manage our roadmap in our centralized Aha! tool, you'll be responsible for creating releases, features, Gathering inputs from various sources including users, sales teams, and forward deployed teams - and prioritizing all of these in our weekly product governance session.
5. Writing user stories: Developing the user stories of how the development team should take forward the release and its underlying features that you have written in the roadmap. You will interface with our development teams in Argentina.
6. UI and UX: you can work with our UI/UX designers to envision, and evolve both the user interface, and the click-through experience of both the ML and GenAI products. 
7. Testing as our machine learning and generative AI teams roll out features, you will need to test out how these work, and provide your input using quantitative and quality measures on quality, and accuracy. We use scoring systems, do adversarial testing, and find other ways to know if our products work., 
8. Interface with the core app team: you will have interdependencies with the product manager for the core real-time Factory floor app, as well as the deaf teams of that product. You'll need to interface to find ways or places where predictions or Generations get deployed.
9. Beta test with users:  you like and are able to spend time with actual users, often in a factory, that are using the product - Gathering input from them. you gather feedback and iterate.
10. Conduct analysis: You can conduct analysis using analytics tools that are tied to the app, to understand usage patterns, where we might have issues, and how to take a data-driven approach to modifying key features

Requirements

You are expected to understand and know the broad steps to develop and improve traditional machine learning - specifically regression and logistic regression. You do not need to be a technical expert, simply understand the recipe, and steps - Ideal candidates will have completed one or several machine learning courses on Coursera, such as Stanford's machine learning course, or the Deeplearning.ai specialization.

Traditional machine learning understanding

• Training data:  you understand what a training data set is, how it needs to be labeled correctly to ensure data quality, and might even be familiar with SQL to review the quality of structured data sets.
• Feature engineering:  you understand how features are added, or developed to enhanced structured data sets, and can think about potential features that could enhance the accuracy of a prediction.
• Model training and serving:  you broadly understand what training involves, and can work with the machine learning engineers so that they retrain models when something has changed in the data inputs.
• Model testing and accuracy: You understand that predictions have varying levels of accuracy, and can intuitively point the machine learning technical team to retrain or enhance data if accuracy is low.
• Serving predictions: You can envision, design, and work with a UI/UX designer to develop the interface where predictions will be served. predictions are often numbers and require dashboard type interfaces or simple ways to show a value
• ML stack: You understand broadly what are the components of a machine learning stack, including pipeline managers, experiment trackers, ML Ops platforms, feature stores, or others. You do not need to know what all these things are, just how to research them and understand what is what in the stack

Overall you're expected to understand what generative AI is, and simply know its key capabilities. You're not expected to be a technical expert, simply to know what these things are and what to look for. Ideal candidates will have completed one or several generative AI courses on coursera, such as generative AI for everyone.

Generative AI understanding

• Use case definition: You understand that generative AI is simply text to create more text, or images to develop images; and therefore can identify use cases where most white collar workers are generating text or graphs.
• Foundational models:  You know what foundational models are and what they do, and how they can be leveraged. you know how to locate and I love them leaderboard, and what makes for a good model.
• Fine-tuning:  You understand the difference between pre-training a model, fine tuning a model, and instruction tuning a model. you do not need to know how to do all these things but just understand what these things are
• RAG -  You understand what RAG is and how it can be used to query documents or deliver other advantages for users. You do not need to be a RAG expert, simply understand what it is and what it does.

Base and Travel

• This job is based in Mexico City, could potentially be based in Bogota or Medellin, Colombia  for the right candidate.

• Travel will be required quarterly, specifically to visit clients, speak to power users, conduct research, or other activities that may yield insights about the product. We serve client factories in Ciudad de México, Guadalajara, Monterrey, Chihuahua.

The Product Deployment Lead is a key member in Allie, responsible for leading the deployment process of our product in manufacturing environments. This role requires a unique combination of technical skills, project management, and the ability to collaborate with cross functional teams to ensure successful delivery of advanced technological solutions to our clients, contributing to the ongoing growth and innovation of Allie.

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Responsibilities:

• Collaborate with cross functional teams to understand project requirements and establish an effective deployment plan.
• Developing a deployment plan to ensure compliance with all the agreed milestones.
• Managing delivery dates, budget and schedules, providing project direction on one or more projects while maintaining a high-quality level of service. Ensuring the quality of all deliverables.
• Managing client expectations and nurturing the client relationship by being the main point of contact for all project-related communications. 
• Identify and manage potential risks, anticipating issues and developing contingency plans.
• Work closely with Account Managers, Automation Engineers and Data Integration teams to ensure seamless integration of solutions into clients' existing environments.
• Lead the Deployment Process from initial phase to final delivery, ensuring customer satisfaction at each stage.
• Manage relationships with vendors, ensuring effective communication and timely delivery of hardware and services.
• Oversee configuration, installation, and testing of solutions, ensuring they meet agreed-upon performance and functionality standards.
• Gather feedback and share lessons learned with the Guild and the Head of Deployment, to identify areas for improvement and opportunities to optimize deployment processes and cycle times.

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Requirements:

• Previous experience in project management roles, preferably in the manufacturing and/or technology industry (expertise in both is heavily preferred).
• Have a technical interest, a knowledge of SaaS companies, and excellent presentation skills.
• Excellent communication abilities and capacity to effectively interact with a variety of teams and clients.
• Strong leadership skills and ability to motivate and guide team members towards common goals.
• Demonstrated ability to manage multiple projects simultaneously and work under pressure in dynamic environments.
• Bachelor's degree in engineering, project management, or a related field (or equivalent experience).

The Data Integration Leader is part of an industry tribe that deploys the software products at the factory, working together with clients. They work through the entire product deployment process from configuring accounts to data validation and resolution, your meticulous attention to detail and client-facing responsibilities directly contribute to the success of our solution. 

We currently serve clients in the food, beverage, and building materials industries. 

Main Responsibilities

1. Understand client operations: Learn client manufacturing operations through walkthroughs, structured questions, adequate note-taking, to build depth around a client’s operation
2. Client stakeholder mapping and management: Map, get to know, and work with clients that are primarily heads of manufacturing, line supervisors, heads of maintenance to build relationships and manage deployment.
3. Product deployment planning: Being part of a product deployment planning team to determine how to best gather data sources and what long lead time items need to be set off early in the process
4. Structured Data sourcing, ingestion, validation, testing for our RealTime Factory OEE product
   • Account configuration data sourcing: Sourcing key data inputs on users, machines, machine speeds, process variables, reasons for downtime, and reasons for quality rejections.
   • Structured Data loading: Using templates, tools, and simple SQL statements to load data into Allie RealTime Factory so that it may display client operations.
5. Structured Data sourcing for Allie ML: Source additional data sets specifically related to: energy consumption, water usage, gas, compressed air, and other plan utilities to aid predictions from Allie ML.
6. Unstructured Data sourcing for FactoryGPT: Gather key prompts and answers that heads of manufacturing, heads of quality, or line supervisors may use in day to day operations to introduce them to FactoryGPT.
7. Process and Machine Data Validation and Testing: Conduct on-site visits to conduct data quality testing and validations to ensure accuracy and reliability of data emerging from machines.
8. Data root cause investigations: Conduct investigations when the data shown by Allie software does not match with the data observed at the plant - this will require pulling data and investigating. 
9. Data accuracy and quality: Once software has been implemented, oversee and continuously improve the quality of data, implementing standards and processes for its management.
10. Soft launch management - During soft launch phase, train up users on how to best use software to consume and label downtime stops, and manage tickets associated with data accuracy.
11. Pilot program problem Resolution: When engaging in a pilot program, engage in client-facing responsibilities, primarily interacting with line supervisors to provide direct support on data-related issues, resolving problems and discrepancies efficiently.
12. Knowledge feedback loop: Crystallize learnings from a deployment into playbooks / documents / repositories or others to reduce cycle time, errors, and increase quality  in future deployments.

Requirements

• Factory life and client hands: We exist to help our clients make meaningful gains in their manufacturing operations. If you like being at a factory and clients this job is for you.
• Teamwork: Can work in a team that is led by a product deployment leader, can work with a technical engineer doing hardware installations, can work with account managers, can work with other Allie members through the deployment.
• Structured Data understanding: You understand structured data sets (no different than a large Excel sheet) and can sort through columns with filters or other simple functions.
• Basic SQL understanding: SQL is a plus, but not required, but awareness or basic knowledge of SQL statements in terms of selecting from tables, grouping data, sorting, and others.
• Initiative to locate data sources: You can work with clients and stakeholders such as IT to gather data that is needed to set up Allie Software. Sometimes these are existing datasets, but also API connections.
• Work with 3rd parties: When data exists in 3rd party sources, such as controllers, work with 3rd party automation partners to open up factory controls and extract variables and data.
• Data gathering / organizing / ingestion: Can keep interim spreadsheets organized, datasets organized, and work with Allie technical teams to load data.
• Data gathering automation: Identify ways to automate ‘account config’ or other data gathering and ingestion to reduce future on the ground visits.
• Good Analytical Skills: Ability to analyze tabular / structured data sets and find root causes. You have the initiative to go and check in a factory floor if the data matches reality.
• Data tools: Highly proficient in Excel, working knowledge or willingness to learn any tool to crunch data such as Tableau, or PowerBI.
• ChatGPT prompting: Knowledge of ChatGPT and ability to input predefined prompts to clean and organize data.
• Technical Support: Background in providing technical support for data-related queries and issues.
• Data Quality Management: Understanding and application of best practices to ensure and maintain high data quality.
• Use of Enterprise Tools: Can use Jira to create and manage tickets, Slack to communicate with teams, Hubspot to update client data. Other enterprise tools may be required. General proficiency with tools

Travels

• Travel likely required for this job. We serve client factories in Ciudad de México, Monterrey, Chihuahua. 
• Demonstrate flexibility and availability for travel to client sites as needed for product deployment and support. 

Allie deploys software products in factories that help manufacturing heads reduce downtime, improve equipment operating efficiency, manage processes, and improve quality. Allie’s products are RealTime Factory Floor which produces real time factory analytics at the line level, FactoryGPT - a Generative AI product to accelerate time to insight, and Allie ML, a machine learning product that delivers predictions about factory outcomes. They are all underpinned by the Allie Gateway, a device that enables real-time data streaming from the factory.

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The Product Integration Leader is part of an industry tribe that deploys the software products at the factory, working together with clients. They work through the entire product deployment process from configuring accounts to data validation and resolution, your meticulous attention to detail and client-facing responsibilities directly contribute to the success of our solution. 

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OKRs
Reduce data accuracy cycle time from soft-launch to launch to 5 days
Ensure a data quality metric upwards of X%
Reduction of data accuracy tickets to 0 post launch 

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We currently serve clients in the food, beverage, and building materials industries. 

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Main Responsibilities

1. Understand client workflows: Learn client operations through walkthroughs, structured questions, adequate note-taking, to build depth around a client’s operation
2. Client stakeholder mapping and management: Map, get to know, and work with clients that are primarily heads of manufacturing, line supervisors, heads of maintenance to build relationships and manage deployment.
3. Product deployment planning: Being part of a product deployment planning team to determine how to best gather data sources.
4. Data sourcing: Sourcing key data inputs on users, machines, process variables and other inputs that power Allie software. Pin point data locations and enablers or blockers to ingest data.
5. Data loading: Using templates, tools, and simple SQL statements to load data into Allie Software so that it may display client operations.
6. Data Validation and Testing: Conduct on-site visits to conduct data quality testing and validations to ensure data accuracy and reliability.
7. Data root cause investigations: Conduct investigations when the data shown by Allie software does not match with the data observed at the plant - this may have different root causes and requires pulling data and investigating. 
8. Insights and Action Plans: Act as the 'Voice of the Supervisor,' analyzing relevant data to provide key insights, and develop data-based action plans to improve operational efficiency and decision-making. 
9. Data accuracy and quality: Once software has been implemented, oversee and continuously improve the quality of data, implementing standards and processes for its management.
10. Soft launch management - During soft launch phase, train up users on how to best use software to consume and label data, and manage tickets associated with data accuracy.
11. Pilot program problem Resolution: When engaging in a pilot program, engage in client-facing responsibilities, primarily interacting with line supervisors to provide direct support on data-related issues, resolving problems and discrepancies efficiently.
12. Knowledge feedback loop: Crystallize learnings from a deployment into playbooks / documents / repositories or others to reduce cycle time, errors, and increase quality  in future deployments.

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Requirements

• Factory life and client hands: We exist to help our clients make meaningful gains in their manufacturing operations. If you like being at a factory and clients this job is for you.
• Teamwork: Can work in a team that is led by a product deployment leader, can work with a technical engineer doing hardware installations, can work with account managers, can work with other Allie members through the deployment.
• Structured Data understanding: You understand structured data sets (no different than a large Excel sheet) and can sort through columns with filters or other simple functions.
• Initiative to locate data sources: You can work with clients and stakeholders such as IT to gather data that is needed to set up Allie Software. Sometimes these are existing datasets, but also API connections.
• Work with 3rd parties: When data exists in 3rd party sources, such as controllers, work with 3rd party automation partners to open up factory controls and extract variables and data.
• Data gathering / organizing / ingestion: Can keep interim spreadsheets organized, datasets organized, and work with Allie technical teams to load data.
• Data gathering automation: Identify ways to automate data gathering and ingestion to reduce future on the ground visits.
• Good Analytical Skills: Ability to analyze tabular / structured data sets and find root causes. You have the initiative to go and check in a factory floor if the data matches reality.
• Data tools: Highly proficient in Excel, working knowledge or willingness to learn any tool to crunch data such as Tableau, or PowerBI.
• ChatGPT prompting: Knowledge of ChatGPT and ability to input predefined prompts to clean and organize data.
• Basic SQL understanding: SQL is a plus, but not required, but awareness or basic knowledge of SQL statements in terms of selecting from tables, grouping data, sorting, and others.
• Technical Support: Background in providing technical support for data-related queries and issues.
• Data Quality Management: Understanding and application of best practices to ensure and maintain high data quality.
• Use of Enterprise Tools: Can use Jira to create and manage tickets, Slack to communicate with teams, Hubspot to update client data. Other enterprise tools may be required. General proficiency with tools

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Deliverables: 

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• Daily Data report: At the closing time of each station, a report must be provided on:
  Irregular activity>Progress/OEE/Avail/Performance over 100% or near 0.
  Stops/Classified Stops. 

• Customer support: End-of week report with all tickets reported/cause and action plan. 
• Voice-of-the-Supervisor: End-of-month report with engagement metrics & insights from each station and action plan. 
• Data quality Report: End-of-week report on data quality metric per station and action plan for each station. 
• Automation of account configuration via tables in SQL: New Machine, New User, New Product.

Travel:

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• Travel is required for this job. We serve client factories in Ciudad de México, Monterrey, Chihuahua. 
• Demonstrate flexibility and availability for travel to client sites as needed for project implementation and support.