Engineering Manager, Data Scientist

ABOUT

The Persona Labs is building a new kind of social platform focused on something most social products do not explicitly optimize for: helping people become real friends.

We want to help people discover interesting people around them, find meaningful common ground, start low-pressure interactions, continue promising conversations, create shared experiences, and ultimately build real-life friendships.

DISCOVER → CURIOSITY → COMPATIBILITY → INTERACTION → UNDERSTAND → IRL EXPERIENCE → FRIENDSHIP

THE AI LAYER - COMPANION INTELLIGENCE

Alongside the platform, we are building a proactive personal AI companion that learns about the user and helps them navigate this journey through personalized recommendations, suggestions, reminders, conversations, and experiences. 

THE OPPORTUNITY

We are looking for a Founding ML Engineer to build the intelligence layer of the platform from the ground up. This is a 0→1 Applied AI / ML role where you will work directly with the founder and Product Engineer to turn ambiguous problems around users, relationships, recommendations and personal intelligence into working systems.

You will be expected to:

Understand the problem → identify the signals → design the intelligence system → prototype → evaluate → deploy → learn → improve.

WHAT YOU WILL BUILD & OWN

USER INTELLIGENCE

User representations, behavioural models, interests, preferences, contextual signals, and evolving understanding of the user. MEMORY Short- and long-term memory, episodic/preference/relationship memory, retrieval, relevance and updating.

RECOMMENDATION & MATCHING

People discovery, compatibility, activity/experience recommendations, and personalized ranking.

INTENT & INTEREST

Infer what the user is trying to do and learn what they care about from behaviour, not only declared interests.

RANKING

Decide what should appear first across potentially thousands of relevant people, activities or experiences.

CONTENT INTELLIGENCE

Classification, toxicity, spam, policy signals, quality, relevance, and semantic understanding.

RELATIONSHIP INTELLIGENCE

Reciprocity, interaction health, shared interests, progression, declining engagement and shared activity.

NEXT-BEST-ACTION

Determine the most useful action now: show a person, suggest a question, recommend an activity, reconnect, or do nothing.

TRUST / SAFETY INTELLIGENCE

Fake-account signals, spam, abuse, behavioural anomalies, risky interactions and moderation assistance.

COMPANION INTELLIGENCE

Use signals and outputs to help the companion decide what to say, suggest, recommend or not do. 

WHAT YOUR DAY-TO-DAY LOOKS LIKE

• Translate ambiguous product problems into ML/AI system designs.

• Build models and intelligence pipelines using behavioural, relational and contextual signals.

• Develop recommendation, matching and personalization systems.

• Design memory and retrieval systems that help the companion understand the user over time.

• Build and evaluate LLM-powered and agentic workflows.

• Decide when to use traditional ML, rules, retrieval, ranking or LLMs.

• Prototype quickly, test assumptions and iterate based on real user behaviour.

• Work closely with the founder and Product Engineer to turn intelligence into product experiences.

• Design APIs and production systems that bring ML/AI capabilities into the application.

• Build evaluation, monitoring and feedback loops so the intelligence improves over time.

WHO SHOULD APPLY

• Experience: 0–4 years’ experience, including exceptional fresh graduates. Strong 1–3 year engineers and experienced 3–4 year product builders are welcome.

• Strong foundations in ML, Python, statistics and software engineering.

• Evidence of Building: Experience with AI/ML projects, recommendation systems, LLM applications or personalization is highly valued.

• Strong evidence of building: Shipped projects, research, hackathons, internships, open source or startup work. 

WHAT WE LOOK FOR

MACHINE LEARNING DEPTH

Can you understand the modelling problem underneath the application?

RECOMMENDATION & PERSONALIZATION

Can you reason about relevance, ranking, cold start and behavioural signals?

AI ENGINEERING

Can you turn LLMs and agents into reliable product capabilities rather than simple API wrappers?

USER INTELLIGENCE

Can you design systems that gradually understand a person from sparse and changing signals?

SYSTEMS THINKING

Can you move from a model to a production system with APIs, data, latency, cost and monitoring?

EVALUATION MINDSET

Can you determine whether the intelligence actually helped the user?

PRODUCT JUDGMENT

Can you decide what the system should do when there is no predefined answer?

SPEED OF EXECUTION

Can you move from idea → prototype → evaluation → production quickly and responsibly?

BUILD WITH US

You will join at a stage where many of the answers do not exist yet. You will not simply implement a model someone else selected; you will help decide how the product learns to understand people.

CAREERS:

Apply with your resume, GitHub, portfolio or shipped work.

https://forms.gle/12YpUSBY2Sqs5xjp8

www.thepersonalabs.com

Analytics Lead – AI & Decision Intelligence

Technical Leadership | People Management | Client Engagement | AI / Data / ML

About the Role

Our client is an enterprise AI and Decision Intelligence product company building advanced solutions for banking,

financial services and insurance. Its work spans predictive analytics, Machine Learning, Generative AI and Agentic AI,

with a strong focus on production-ready deployment, explainability, governance and measurable business outcomes.

The company is looking for an experienced Analytics Lead – AI & Decision Intelligence to lead a multidisciplinary team

of Data Scientists, ML Engineers, AI Engineers and other technical specialists.

This is a technical leadership, people management and client-facing role. The successful candidate is not expected to

be a hands-on expert in every area of AI, Data Science and Machine Learning. Instead, the ideal person will bring deep

expertise in at least one core AI/Data/ML discipline, together with the technical breadth and managerial capability to

lead specialists across multiple domains.

You will provide direction to technical teams, translate business problems into AI-led solutions, drive successful

delivery, and act as a key interface between highly technical teams and clients. A critical part of the role is the ability to

simplify complex technical concepts and communicate them clearly to senior and non-technical stakeholders.

What You’ll Do

 Lead and manage a multidisciplinary team of Data Scientists, ML Engineers, AI Engineers and Analytics

professionals.

 Provide technical direction across AI, Machine Learning and Data Science initiatives while enabling specialists to

own their areas of expertise.

 Translate business and customer requirements into appropriate analytical and AI solution approaches.

 Own delivery across scope, priorities, timelines, technical quality and business outcomes.

 Review and challenge technical approaches, solution designs and architecture without needing to be the hands-on

specialist in every area.

 Mentor, develop and motivate technical team members and build a strong culture of collaboration and

accountability.

 Establish and reinforce best practices around model development, deployment, governance, monitoring and

continuous improvement.

 Work closely with Product, Engineering, Sales and Customer Success teams on solution development and product

roadmaps.

 Lead client workshops, technical discussions and solution presentations.

 Explain complex AI and analytics concepts in simple, business-friendly language to senior and non-technical

stakeholders.

 Support pre-sales, solutioning and customer-facing technical engagements.

Technical Capabilities

Candidates should have strong exposure to the broader AI and Data Science ecosystem. Relevant capabilities include:

 Machine Learning & Deep Learning

 Statistical Modeling

 Python and SQL

 Data Science frameworks such as Scikit-learn, XGBoost, LightGBM, TensorFlow or PyTorch

 Data Engineering concepts and Feature Engineering

 Predictive and Prescriptive Analytics

 Model Explainability

 MLOps, CI/CD, model deployment and monitoring

 Generative AI and Large Language Models (LLMs)

 Retrieval-Augmented Generation (RAG)

 AI Agents and Agentic AI architectures

Analytics Lead – AI & Decision Intelligence | LinkedIn Job Description

 Prompt Engineering

 Responsible AI, model governance and regulatory considerations

 Cloud platforms such as AWS, Azure or Google Cloud

Important: You do not need to be an expert in all of the above. We are looking for deep expertise in at least one core

area of AI, Data Science, Machine Learning, ML Engineering, Generative AI, MLOps or Data Engineering, combined with

sufficient breadth to effectively lead experts in adjacent areas.

What We’re Looking For

 5–8 years of overall experience in AI, Data Science, Machine Learning, Analytics, Data Engineering or a related

technology discipline.

 Proven experience leading or managing teams of Data Scientists, ML Engineers, AI Engineers or similar technical

professionals.

 Strong people management, mentoring, delegation and team-building capabilities.

 Experience leading complex technical projects involving multiple specialist disciplines.

 Strong client-facing, stakeholder management and project delivery skills.

 Ability to communicate technical concepts clearly to non-technical audiences.

 Strong presentation skills and confidence engaging senior business stakeholders.

 Ability to balance technical considerations with business priorities and delivery objectives.

 Strong problem-solving, decision-making, organisational and managerial skills.

Preferred Experience

 Experience delivering AI or analytics solutions in Banking, Financial Services or Insurance.

 Exposure to use cases such as credit risk, fraud detection, customer acquisition, marketing analytics, collections,

deposits or pricing analytics.

 Experience working in an AI product, technology consulting or enterprise analytics environment.

 Exposure to Generative AI, LLM and Agentic AI programmes.

 Understanding of Responsible AI, explainability and regulatory considerations.

Education

 Bachelor’s or Master’s degree in Engineering, Computer Science, Data Science, Statistics, Mathematics or a

related discipline.

 MBA preferred, but not essential.

The Profile We’re Looking For

We are looking for a technical leader who can lead experts — not someone expected to be the expert in everything.

Technical Depth + People Leadership + Client Management + Delivery Ownership

If you have strong expertise in one area of AI/Data/ML, have successfully managed technical teams, and can

confidently engage with both technologists and business leaders, we would like to hear from you.

Job Description:

We are seeking a versatile and highly skilled Lead AI/ML Engineer with deep expertise in Generative AI (GenAI) and Large Language Models (LLMs). This role requires a leader who can take full ownership of the

AI lifecycle—from initial architectural design to final production execution. You will lead the development of scalable AI-powered applications, demonstrating exceptional execution skills and the ability to deliver high-performance results under pressure in demanding production environments.

Machine Learning & LLM Capability:

 End-to-End ML Engineering: Build and manage comprehensive ML pipelines, including data ingestion, preprocessing, training, and evaluation using frameworks like PyTorch, TensorFlow, and Scikit-learn.  Advanced LLM Systems: Design and implement sophisticated LLM-based applications such as autonomous agents, chatbots, and complex automation tools.

 Generative AI Specialization: Architect and optimize Retrieval-Augmented Generation (RAG) pipelines using vector databases like FAISS, Pinecone, or Weaviate.

 Model Optimization: Fine-tune open-source and proprietary models (e.g., LLaMA, GPT) using advanced techniques like LoRA, QLoRA, or instruction tuning.

 Agentic Frameworks: Develop complex agentic workflows utilizing frameworks such as LangChain or LlamaIndex.

 Prompt Engineering: Implement expert-level prompt engineering, tool/function calling, and structured output generation.

 Project Ownership & Execution

 Full Lifecycle Ownership: Take complete accountability for the full ML and GenAI lifecycle, spanning data processing, model development, monitoring, and optimization.

 Architectural Leadership: Drive strategic architectural decisions for AI platforms, ensuring they are modular, scalable, and maintainable.

 Execution Excellence: Write clean, high-performance Python code following strict OOP principles and manage CI/CD pipelines for seamless project execution.

 Leadership & Mentoring: Act as a key technical leader, managing stakeholders and mentoring team members to ensure all project milestones are met with quality.

 System Integrity: Manage model and prompt versioning, experiment tracking, and comprehensive documentation for all pipelines and workflows.

 Performance Under Pressure

 Production Reliability: Ensure all AI systems maintain extreme scalability and performance under heavy production workloads, including both batch and real-time processing.

 High-Pressure Optimization: Rapidly optimize inference latency and system costs for ML and LLM systems to meet urgent business and technical requirements.

 Proactive Problem Solving: Apply strong analytical thinking to address complex challenges such as system drift, hallucinations, and latency in fast-paced environments.

 Robust Guardrails: Implement and manage strict evaluation frameworks and feedback loops to maintain system quality under stress.

Qualifications:

 Bachelor’s or Master’s degree in Computer Science, AI, ML, or a related field.

 Proven expertise in Python, system design, and scalable AI/ML architecture.

 Deep knowledge of NLP, Computer Vision, and Deep Learning models.

 Hands-on experience with Docker, Kubernetes, MLOps, and major cloud platforms (AWS, GCP, or Azure).