Data Scientist

Qlik • Boston, MA

10h • Hybrid

About The Position

A Gartner® Magic Quadrant™ Leader for 15 years in a row, Qlik transforms complex data landscapes into actionable insights, driving strategic business outcomes. Serving over 40,000 global customers, our portfolio leverages pervasive data quality and advanced AI/ML capabilities that lead to better decisions, faster. We excel in integration and governance solutions that work with diverse data sources, and our real-time analytics uncover hidden patterns, empowering teams to address complex challenges and seize new opportunities. As a Data Scientist on Qlik’s AI Practice team, you will design, train, and deploy machine learning models that power predictive intelligence across Qlik’s business. Reporting to the Global Head of AI Strategy, Policy, and Governance, you will work at the intersection of data engineering, applied ML, and business strategy — transforming raw signals from Qlik’s data ecosystem into models that drive measurable outcomes for customers and internal stakeholders alike. This role offers high-impact data, end-to-end ownership of the ML lifecycle on a modern AWS stack, strategic visibility informing executive decision-making, and a collaborative team environment within a small, high-velocity AI Practice team.

Requirements

• Python is your primary language. You write clean, well-structured code and are comfortable owning end-to-end ML workflows — from data ingestion and EDA through model training, validation, and deployment.
• Practical, hands-on experience with SageMaker as your primary ML platform — including SageMaker Studio, Training Jobs, Pipelines, Model Registry, and real-time or batch Inference Endpoints.
• Strong grounding in supervised and unsupervised ML methods — gradient boosting, neural networks, dimensionality reduction, clustering, and survival/time-to-event models.
• Experience with frameworks such as scikit-learn, XGBoost, LightGBM, and PyTorch or TensorFlow.
• Demonstrated ability to extract, clean, and engineer features from complex, multi-source datasets using Python (pandas, numpy, PySpark) and SQL against platforms such as Snowflake or similar cloud data warehouses.
• Rigorous approach to model evaluation — cross-validation, holdout testing, calibration, and business-metric alignment.
• Experience with experiment tracking tools such as MLflow or SageMaker Experiments.
• Solid AWS experience beyond SageMaker, including S3, IAM, Lambda, and Step Functions.
• Hands-on experience working with Snowflake, Apache Iceberg, or similar modern data platforms as upstream data sources for ML pipelines.
• Bias for Impact: You care about whether your models actually change decisions — not just whether they score well on a leaderboard.
• Strong Communication: Ability to explain model behavior, limitations, and business implications to non-technical stakeholders clearly and without jargon.
• Security and Governance Mindset: Awareness of responsible AI practices, data privacy considerations, model auditability, and the importance of reproducibility in production ML systems.
• Collaborative Spirit: Comfortable working across functions and levels, from data engineers and CSMs to the C-suite.

Nice To Haves

• Familiarity with infrastructure-as-code or CI/CD patterns for ML pipelines.
• Familiarity with Qlik Cloud Analytics or Qlik Talend Cloud.

Responsibilities

• Build and Deploy ML Models: Design, train, evaluate, and deploy supervised and unsupervised machine learning models on AWS SageMaker — including classification, regression, clustering, and anomaly detection use cases.
• Own the Feature Engineering Pipeline: Develop robust, reusable feature pipelines in Python that transform raw data from Snowflake, Qlik Cloud Analytics, and other sources into high-quality model inputs.
• Integrate with the Data Ecosystem: Connect model pipelines to Qlik Cloud Analytics, Qlik Talend Cloud, Snowflake, and Apache Iceberg, ensuring data freshness, lineage, and governance standards are met.
• Operationalize Models at Scale: Leverage SageMaker Pipelines, Model Registry, and Endpoints to bring models into production reliably — with monitoring, drift detection, and retraining workflows in place.
• Support LLM-Augmented Workflows: Collaborate with AI Systems Engineers to integrate predictive model outputs as structured signals into agentic AI pipelines deployed on AWS Bedrock.
• Translate Signals into Action: Partner with Customer Success, Sales, and Analytics stakeholders to translate model outputs into actionable insights, dashboards, and automated intervention triggers.
• Iterate and Instrument: Operate in a fast-moving incubator environment — prototype quickly, measure model performance against business outcomes, and continuously refine based on real usage signals.
• Document and Govern: Maintain clear model cards, experiment logs, and data lineage documentation in support of Qlik’s AI governance framework and ISO 42001 compliance posture.

Benefits

• Genuine career progression pathways and mentoring programs.
• Culture of innovation, technology, collaboration, and openness.
• Flexible, diverse, and international work environment.
• Giving back is a huge part of our culture. Alongside an extra “change the world” day plus another for personal development, we also highly encourage participation in our Corporate Responsibility Employee Programs.
• Comprehensive benefits, including - but not limited to - medical, dental, and vision coverage life and AD&D, short and long-term disability coverage, paid time off, paid parental / maternity leave, participation in a 401(k) program that includes company match, and many other additional voluntary benefits.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed