Data Scientist

Requirements

• Bachelor's degree in Data Science, Computer Science, Mathematics, Statistics, Economics, or a related quantitative field.
• 2+ years of experience in Data Science, Data Engineering, Machine Learning, or Big Data development.
• Strong programming skills in Python and experience with PySpark.
• Hands-on experience working with large-scale data processing frameworks and machine learning pipelines.
• Experience with ML lifecycle and workflow management tools such as MLflow, Airflow, or similar platforms.
• Strong analytical, problem-solving, and statistical modeling skills.
• Ability to communicate complex technical concepts to both technical and non-technical audiences.

• Experience working directly with customers or external stakeholders is a plus.

Preferred Qualifications

• Experience in financial services, fraud detection, risk analytics, compliance, or transaction monitoring.
• Familiarity with cloud-based data and machine learning environments.
• Experience investigating anomalies and extracting insights from high-volume datasets.
• Knowledge of machine learning model deployment and monitoring best practices.

We are seeking a highly motivated Data Scientist to join our growing team and help deliver innovative machine learning and analytics solutions for complex business challenges. In this customer-facing role, you will work with large-scale datasets, develop advanced analytical models, and collaborate cross-functionally with product, engineering, and business stakeholders to drive measurable impact.

Responsibilities

• Design and implement data science solutions throughout the project lifecycle.
• Analyze large and complex datasets to identify trends, anomalies, and actionable insights.
• Develop and optimize machine learning models and advanced analytics workflows.
• Perform feature engineering and data preparation for predictive modeling initiatives.
• Collaborate with Product, Engineering, and Customer Success teams to translate business requirements into technical solutions.
• Lead the data science aspects of customer implementations, pilots, and production deployments.
• Build strong technical relationships with customers and external partners.
• Provide recommendations to improve products, algorithms, and analytical capabilities.
• Deliver training and knowledge transfer to end users and stakeholders.
• Support investigation and resolution of data-related and analytical challenges.

• Python
• PySpark
• Apache Spark
• Machine Learning
• MLflow
• Airflow
• SQL
• Big Data Analytics
• Feature Engineering
• Statistical Analysis