Bridging the Gap Between Theory and Production
At Sabalynx, we don’t believe in “AI for the sake of AI.” Our clients approach us when they need to solve multi-million dollar inefficiencies through algorithmic intervention. As a Data Scientist in our Enterprise AI division, you are the technical lead responsible for the entire lifecycle of an AI asset.
You will be expected to navigate complex, often fragmented enterprise data ecosystems, identify the signal within the noise, and build models that are not only accurate but also interpretable, scalable, and resilient to data drift. You aren’t just writing scripts; you are engineering competitive advantages.

Why This Role Exists
The industry is saturated with “notebook data scientists” who struggle to move models beyond the local environment. Sabalynx requires practitioners who understand that a model’s value is zero until it is integrated into a business process.
Engineering
Statistics
Business Strategy

Core Functions
Key Responsibilities
Your day-to-day involves deep technical execution and high-level stakeholder management.

End-to-End Pipeline Engineering
Design and implement robust ETL/ELT pipelines to ingest, clean, and transform unstructured and structured data at petabyte scale using Spark, Dask, or Snowflake.

Advanced Model Development
Develop bespoke Machine Learning models (XGBoost, LightGBM, Transformers) tailored to specific KPIs like customer churn, predictive maintenance, or fraud detection.

RAG & Generative AI Architecture
Architect Retrieval-Augmented Generation (RAG) systems using vector databases (Pinecone, Weaviate) and LLMs to unlock proprietary knowledge bases for enterprise clients.

MLOps & Deployment
Collaborate with DevOps teams to containerize models (Docker, Kubernetes) and establish CI/CD/CT (Continuous Training) pipelines to automate model redeployment.

Observability & Interpretability
Implement monitoring frameworks for model performance, data drift, and bias detection. Utilize SHAP or LIME to provide explainability for high-stakes decisions.

Strategic Client Consulting
Translate complex technical results into actionable business insights for C-suite stakeholders, justifying AI spend through clear ROI mapping.

A/B Testing & Evaluation
Design rigorous experimental frameworks to validate model performance in real-world environments against established baselines and control groups.

Technical Leadership
Mentor junior data scientists and engineers, conducting code reviews and promoting engineering best practices across the Sabalynx global AI guild.

Requirements
Required Skills & Experience

Master’s or PhD in Computer Science, Statistics, Physics, or a related quantitative field.
5+ years of experience building and deploying machine learning models in a production environment.
Advanced proficiency in Python and the PyData stack (Pandas, NumPy, Scikit-Learn, PyTorch/TensorFlow).
Strong SQL skills and experience with distributed computing frameworks like Apache Spark.
Deep understanding of cloud architectures (AWS, Azure, or GCP) for ML infrastructure.

Bonus Points
Nice-to-Have Skills

Experience fine-tuning Large Language Models (LLMs) and working with LangChain or LlamaIndex.
Contributions to open-source ML libraries or a strong portfolio of independent research/Kaggle performance.
Prior experience in specialized domains such as Quantitative Finance or Bioinformatics.
Familiarity with Infrastructure as Code (Terraform) and MLOps tools like MLflow or Weights & Biases.

The Sabalynx Experience
What We Offer
We provide an environment where technical excellence is the only currency that matters.

Radical Autonomy
We hire experts so we don’t have to micromanage. You own your technical decisions and your architecture.

Elite Peer Group
Work alongside ex-FAANG engineers, PhD researchers, and world-class technology consultants.

High Stakes
No internal “maintenance” projects. Every engagement is a high-visibility transformation for a global industry leader.

Apply for this Position
View All Roles

Engineering the Future of Intelligence
A Career at the Intersection of Bayesian Logic and Production Scale

At Sabalynx, Data Science is not a siloed research function—it is the central nervous system of our global transformation engine. We don’t hire theorists who stay within the confines of Jupyter notebooks; we hire practitioners who understand the entire ML lifecycle, from stochastic modeling and feature engineering to low-latency inference and MLOps orchestration.

Joining our team means operating in an elite environment where “good enough” is a failure metric. You will be tasked with architecting RAG pipelines for Fortune 100s, fine-tuning domain-specific LLMs for high-compliance sectors, and deploying predictive models that manage hundreds of millions in capital. We trade in measurable ROI, not speculative metrics.

Advanced MLOps Stack
Work with state-of-the-art tooling including Kubernetes, Kubeflow, Weights & Biases, and vector databases like Qdrant and Pinecone to ensure models are reproducible, scalable, and monitored for data drift in real-time.

High-Stakes Decision Intelligence
Your models won’t sit on a shelf. You will develop agentic AI systems that automate complex reasoning tasks in industries ranging from quantitative finance to predictive healthcare diagnostics.

The Sabalynx Advantage
Why Lead Here?

Question

Bridging the Gap Between Theory and Production
        At Sabalynx, we don&#8217;t believe in &#8220;AI for the sake of AI.&#8221; Our clients approach us when they need to solve multi-million dollar inefficiencies through algorithmic intervention. As a Data Scientist in our Enterprise AI division, you are the technical lead responsible for the entire lifecycle of an AI asset.
        You will be expected to navigate complex, often fragmented enterprise data ecosystems, identify the signal within the noise, and build models that are not only accurate but also interpretable, scalable, and resilient to data drift. You aren&#8217;t just writing scripts; you are engineering competitive advantages.

Why This Role Exists
          The industry is saturated with &#8220;notebook data scientists&#8221; who struggle to move models beyond the local environment. Sabalynx requires practitioners who understand that a model&#8217;s value is zero until it is integrated into a business process.
          Engineering
          Statistics
          Business Strategy

Core Functions
    Key Responsibilities
    Your day-to-day involves deep technical execution and high-level stakeholder management.

End-to-End Pipeline Engineering
          Design and implement robust ETL/ELT pipelines to ingest, clean, and transform unstructured and structured data at petabyte scale using Spark, Dask, or Snowflake.

Advanced Model Development
          Develop bespoke Machine Learning models (XGBoost, LightGBM, Transformers) tailored to specific KPIs like customer churn, predictive maintenance, or fraud detection.

RAG &#038; Generative AI Architecture
          Architect Retrieval-Augmented Generation (RAG) systems using vector databases (Pinecone, Weaviate) and LLMs to unlock proprietary knowledge bases for enterprise clients.

MLOps &#038; Deployment
          Collaborate with DevOps teams to containerize models (Docker, Kubernetes) and establish CI/CD/CT (Continuous Training) pipelines to automate model redeployment.

Observability &#038; Interpretability
          Implement monitoring frameworks for model performance, data drift, and bias detection. Utilize SHAP or LIME to provide explainability for high-stakes decisions.

Strategic Client Consulting
          Translate complex technical results into actionable business insights for C-suite stakeholders, justifying AI spend through clear ROI mapping.

A/B Testing &#038; Evaluation
          Design rigorous experimental frameworks to validate model performance in real-world environments against established baselines and control groups.

Technical Leadership
          Mentor junior data scientists and engineers, conducting code reviews and promoting engineering best practices across the Sabalynx global AI guild.

Requirements
        Required Skills &#038; Experience
        
          Master’s or PhD in Computer Science, Statistics, Physics, or a related quantitative field.
          5+ years of experience building and deploying machine learning models in a production environment.
          Advanced proficiency in Python and the PyData stack (Pandas, NumPy, Scikit-Learn, PyTorch/TensorFlow).
          Strong SQL skills and experience with distributed computing frameworks like Apache Spark.
          Deep understanding of cloud architectures (AWS, Azure, or GCP) for ML infrastructure.

Bonus Points
        Nice-to-Have Skills
        
          Experience fine-tuning Large Language Models (LLMs) and working with LangChain or LlamaIndex.
          Contributions to open-source ML libraries or a strong portfolio of independent research/Kaggle performance.
          Prior experience in specialized domains such as Quantitative Finance or Bioinformatics.
          Familiarity with Infrastructure as Code (Terraform) and MLOps tools like MLflow or Weights &#038; Biases.

The Sabalynx Experience
      What We Offer
      We provide an environment where technical excellence is the only currency that matters.

Radical Autonomy
        We hire experts so we don&#8217;t have to micromanage. You own your technical decisions and your architecture.

Elite Peer Group
        Work alongside ex-FAANG engineers, PhD researchers, and world-class technology consultants.

High Stakes
        No internal &#8220;maintenance&#8221; projects. Every engagement is a high-visibility transformation for a global industry leader.

Apply for this Position
      View All Roles

Engineering the Future of Intelligence
        A Career at the Intersection of Bayesian Logic and Production Scale
        
          At Sabalynx, Data Science is not a siloed research function—it is the central nervous system of our global transformation engine. We don&#8217;t hire theorists who stay within the confines of Jupyter notebooks; we hire practitioners who understand the entire ML lifecycle, from stochastic modeling and feature engineering to low-latency inference and MLOps orchestration.

Joining our team means operating in an elite environment where &#8220;good enough&#8221; is a failure metric. You will be tasked with architecting RAG pipelines for Fortune 100s, fine-tuning domain-specific LLMs for high-compliance sectors, and deploying predictive models that manage hundreds of millions in capital. We trade in measurable ROI, not speculative metrics.

Advanced MLOps Stack
              Work with state-of-the-art tooling including Kubernetes, Kubeflow, Weights &#038; Biases, and vector databases like Qdrant and Pinecone to ensure models are reproducible, scalable, and monitored for data drift in real-time.

High-Stakes Decision Intelligence
              Your models won&#8217;t sit on a shelf. You will develop agentic AI systems that automate complex reasoning tasks in industries ranging from quantitative finance to predictive healthcare diagnostics.

The Sabalynx Advantage
          Why Lead Here?

Accepted Answer

20+ Countries with active deployments requiring diverse data strategies. Zero Legacy constraints. We build greenfield AI architectures designed for 2025 and beyond. $500M+ Aggregate value generated for clients through automated ML pipelines. &#8220;We look for Data Scientists who possess the rare intersection of mathematical rigor and software engineering discipline. If you can&#8217;t containerize your model, you aren&#8217;t done yet.&#8221; — Chief Technology Officer, Sabalynx The Evaluatio

Data Scientist
Enterprise AI

Data Scientist,
Enterprise AI

Bridging the Gap Between Theory and Production

Why This Role Exists

Key Responsibilities

End-to-End Pipeline Engineering

Advanced Model Development

RAG & Generative AI Architecture

MLOps & Deployment

Observability & Interpretability

Strategic Client Consulting

A/B Testing & Evaluation

Technical Leadership

Required Skills & Experience

Nice-to-Have Skills

What We Offer

Radical Autonomy

Elite Peer Group

High Stakes

A Career at the Intersection of Bayesian Logic and Production Scale

Advanced MLOps Stack

High-Stakes Decision Intelligence

Why Lead Here?

The Sabalynx Interview Architecture

Algorithmic & Statistical Baseline

ML System Design & Architecture

The Enterprise AI Case Study

Executive Strategic Alignment

Pre-Interview Requirement

Ready to Deploy Intelligence?

Ready to Deploy Data Scientist Enterprise AI?

Data Scientist Enterprise AI

Data Scientist,Enterprise AI