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About the Role

This role requires high-level autonomy, where you will define your own direction within strategic goals, influence multiple teams, and navigate high complexity and ambiguity to deliver tangible business results. You will bridge the gap between technical innovation and real-world business challenges by applying advanced statistical and machine learning techniques to build predictive models and derive actionable insights.

Responsibilities

Define your own direction within strategic goals
Influence multiple teams
Navigate high complexity and ambiguity to deliver tangible business results
Bridge the gap between technical innovation and real-world business challenges
Apply advanced statistical and machine learning techniques to build predictive models
Derive actionable insights
Design end-to-end ML systems for complex problems
Apply advanced statistical methods
Translate seamlessly between business needs and engineering solutions
Build complete applications rapidly across any technology stack
Select the right tools to balance technical debt with delivery speed
Architect scalable data strategies across diverse teams and complex enterprise landscapes
Define data governance policies
Master data quality frameworks
Build robust integration solutions for undocumented schemas and disparate systems
Drive the adoption of modern data warehousing and lakehouse architectures
Lead AI governance initiatives
Create AI evaluation standards
Train teams on rigorous AI verification and risk management
Lead rapid delivery initiatives using a prototype-first approach
Validate solutions quickly before scaling
Seek out undefined problems
Embed with users to discover latent needs
Turn ambiguity into clear problem statements

Requirements

Bachelor's degree in Computer Science or Engineering
8–12 years of relevant experience leading complex technical projects with multi-team impact
Expert capability in designing end-to-end ML systems for complex problems (including imbalanced data and concept drift)
Demonstrate a deep understanding of causal inference techniques, experimentation design (A/B testing, MABs), and deep learning architectures for unstructured data (e.g., NLP, Computer Vision)
Expertise in Python (including libraries like Pandas and NumPy) or R
Proficiency with cloud platforms and their data/ML services (e.g., AWS Sagemaker, GCP Vertex AI, Azure ML Services)
Experience with big data technologies (e.g., Spark, Hadoop)
Familiarity with MLOps tools (e.g., Kubeflow, MLflow, Docker)
Proven ability to immerse in operations to acquire deep domain expertise
Ability to build complete applications rapidly across any technology stack
Experience architecting scalable data strategies across diverse teams and complex enterprise landscapes
Experience leading AI governance initiatives
Proficiency in leading rapid delivery initiatives using a prototype-first approach
A track record of seeking out undefined problems
Bridge gaps between engineering, design, business, and science
Rapidly immerse yourself in new domains to speak the language of the business
Seek out ambiguity rather than avoiding it
Drive team curiosity through challenging questions
Create an environment that encourages experimentation
Drive an accountability culture focused on business impact rather than just deliverables
Own relationships and make trade-offs between custom solutions and generalizable work
Map complex system interactions across technical and business domains
Anticipate cascading effects and understand how technology changes impact operations
Translate seamlessly between technical and business language
Ensure requirements are clear enough to enable AI generation and facilitate productive discussions with stakeholders
Embed with users to discover latent needs
Turn ambiguity into clear problem statements rather than waiting for defined tasks

Skills

Machine Learning
Statistics
Causal Inference
Experimentation Design (A/B testing, MABs)
Deep Learning (NLP, Computer Vision)
Python (Pandas, NumPy)
R
Cloud Platforms (AWS, GCP, Azure)
Big Data Technologies (Spark, Hadoop)
MLOps (Kubeflow, MLflow, Docker)
Data Strategy
Data Integration
Data Governance
Data Quality
AI Governance
Rapid Prototyping
Problem Discovery

Location

Hybrid

Work Type

Hybrid

Experience Level

8-12 years

Education Level

Bachelor's degree in Computer Science or Engineering

Benefits

Challenging and rewarding work with real impact
Direct Access to Cutting-Edge AI Platforms
Diverse and Inclusive Culture
Growth opportunities for personal and professional development
A collaborative and innovative work environment where your ideas are valued
Exposure to exciting projects and high-profile clients
Supportive work environment with access to company leaders

About the Company

Appnovation is a global, full-service digital partner that combines Strategy, Experience & Design, Engineering and Managed Services.
We build digital solutions that deliver real impact today and serve as foundations for future growth.
Bold ambition. Practical action. Endless possibilities.

Equal Opportunity

At Appnovation, we recognize that diverse teams are the strongest teams. Diversity, Equity & Inclusion is not only something that we embrace - we celebrate it!
We are proud to be an Equal Opportunity Employer and we encourage applicants from all backgrounds, lived experiences and industries to apply.
Come join us at Appnovation, and learn more about how we stay true to our company values as we build better lives through better digital.
Accommodations are available upon request throughout the recruitment process.