Data Scientist( Mid) in Ensitech

Join #Ensiteam!

We are a company focused on custom software development and have been in the market for over 19 years. Ensitech is dedicated to finding highly specialized talent to work with large companies in the United States, Europe, and Canada.

Each of our collaborators works with different teams and colleagues around the world, in the design and commercialization of software. We look forward to having you as part of our team as a Backend Lead software engineer.

• Customer Behavior Analysis: Analyze customer transaction patterns, spending habits, and behavior to identify trends and anomalies.
• Risk Detection: Develop models to detect potential risks such as fraud, defaults, or other types of anomalies that affect the business.
• Graph Analytics: Study and analyze data represented in graphs (e.g., network analysis, path analysis) to uncover behavioral patterns and detect unusual activities.
• Anomaly Detection: Design and implement algorithms for anomaly detection, focusing on both financial risk and customer experience anomalies.
• Collaboration with Stakeholders: Collaborate with risk, marketing, and product teams to understand data needs and deliver actionable insights.
• Data Exploration & Reporting: Use data visualization tools to present findings and drive data-driven decision-making within the organization.
• Predictive Modeling: Build and refine predictive models using machine learning techniques to forecast customer behavior and assess credit risk.
• Data Management: Work with large datasets, ensuring data quality, integrity, and accessibility for analysis.
• Analytical Thinking: Strong problem-solving skills with the ability to think critically and work with complex datasets.
• Communication Skills: Excellent verbal and written communication skills, with the ability to present technical concepts to non-technical stakeholders.

• Education: Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
• Experience: 2-3 years of experience in data analysis, preferably in financial services or risk management.
• Programming Skills: Python or R for data analysis + libraries such as pandas, NumPy, sci-kit-learn, or similar.
• Graph Analysis: Experience analyzing data represented in graphs, including graph-based anomaly detection techniques (e.g., network analysis, graph algorithms).
• Anomaly Detection: Familiarity with techniques for identifying anomalies in large datasets.
• Advanced Graph Algorithms: Experience with specialized tools for graph analysis such as Neo4j, or similar.
• Data Visualization: Strong skills in data visualization using tools like Matplotlib, Seaborn, Plotly, or similar.
• Machine Learning Techniques: Knowledge of machine learning techniques for risk modeling and fraud detection (e.g., unsupervised learning, clustering techniques).
• Cloud Platforms: Experience with cloud-based data environments like AWS, GCP, or Azure.
• Big Data Tools: Familiarity with big data tools and platforms such as Hadoop, Spark, or similar technologies.
• Financial Domain Expertise: Previous experience in the financial services industry, specifically in credit risk analysis or fraud detection.
• SQL Proficiency: Strong experience working with databases and writing complex SQL queries for data extraction.

• Salary adapted to your needs.
• Vacations.
• Certifications.
• 100% remote opportunity.
• Bonuses .