Data Scientist

Daxwell; Houston (TX); On-site; USD 140,000 - 190,000; Full time

Job summary

Daxwell is seeking an experienced Data Scientist to implement machine learning initiatives in an onsite role based in Houston, TX. You will build scalable pipelines, operationalize end-to-end ML workflows, and turn complex data into production-ready models.

You will collaborate with engineering, analytics, and product teams to deploy models and drive data-driven decision making across enterprise applications.

Qualifications

• Master’s or Ph.D. in a quantitative field (CS/Stats/Math).
• Advanced proficiency in Python, PySpark, Pandas, PyTorch.
• Solid ML foundation including GBT and DL architectures (MLP, RNN, Transformers).
• Familiarity with lakehouse architectures Delta Lake and Apache Iceberg.
• Experience with data orchestration/streaming (Airflow, Flink, Kafka); cloud ML deployments (EKS, EMR).
• Strong communication to translate complex insights for stakeholders.

Responsibilities

• Architect scalable data ingestion, transformation, and training pipelines.
• Prepare, clean, and feature-engineer large-scale data sets; ensure quality and reproducibility.
• Conduct inference, EDA, and causal validation to generate actionable insights.
• Select, train, validate, and deploy supervised and unsupervised models.
• Build transfer learning and fine-tuning pipelines with pretrained transformers.
• Design and automate end-to-end ML workflows: training, evaluation, feature versioning, serving.

Education

Master’s or Ph.D. in Computer Science, Statistics, Mathematics, or related quantitative discipline

Tools

Python

PySpark

Pandas

PyTorch

Delta Lake

Apache Iceberg

Airflow

Flink

Kafka

EKS

EMR

GBT

MLP

RNN

Transformers

Job description

Daxwell is seeking an experienced Data Scientist to implement machine learning initiatives in an onsite role based in Houston, TX. In this position, you will help turn complex data into production-ready models by building scalable pipelines and operationalizing end-to-end ML workflows.

Role focus

• Architect and implement scalable data ingestion, transformation, and training pipelines using distributed processing frameworks and orchestrators.
• Collect, clean, normalize, and feature-engineer structured, semi-structured, and unstructured datasets at scale, with emphasis on data quality and reproducibility.
• Conduct statistical inference, exploratory data analysis (EDA), and causal validation to generate actionable and interpretable insights from complex data systems.
• Select, train, validate, and deploy supervised and unsupervised models using modern ML frameworks.
• Build and maintain transfer learning and fine-tuning pipelines that leverage pretrained transformer architectures.
• Design, optimize, and automate end-to-end ML workflows, including model training, evaluation, feature versioning, and model serving in production environments.
• Partner with engineering, analytics, and product teams to operationalize data-driven solutions and integrate predictive models into enterprise applications.

Requirements

• Master’s or Ph.D. degree in Computer Science, Statistics, Mathematics, or a related quantitative discipline.
• Advanced proficiency in Python, PySpark, Pandas, and PyTorch.
• Strong foundation in machine learning algorithms, including GBT and deep learning architectures such as MLP, RNN, and Transformers.
• Familiarity with lakehouse architecture best practices, including Delta Lake and Apache Iceberg.
• Experience with data orchestration or streaming frameworks (e.g., Airflow, Flink, Kafka); familiarity with cloud-native ML deployments (e.g., EKS, EMR) is a plus.
• Strong communication skills to translate complex technical insights into clear, actionable recommendations for business stakeholders.

Technologies

• Python, PySpark, Pandas, PyTorch
• GBT, MLP, RNN, Transformers
• Delta Lake, Apache Iceberg
• Airflow, Flink, Kafka
• EKS, EMR