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Junior Data Scientist

DetaPent • Full-time • Remote (South Carolina, United States, US) • $60k - $90k / year • 3d ago

Title: Jr. Data Scientist / AI-ML Engineer

Location: Columbia, SC (Remote)

Duration: Full Time.

Job description:

Key Responsibilities:

• Collaborate with stakeholders to understand business objectives and define requirements for anomaly detection.

• Develop, optimize, and maintain computational models for debit transaction anomaly detection using AI/ML techniques.

• Perform data analysis, generate insights, and identify patterns to support decision-making.

• Design and implement statistical models, including standard deviation calculations, variance thresholds, and probabilistic models to enhance anomaly detection accuracy.

• Work with existing models to apply backtracking methodologies and improve anomaly reduction strategies.

• Leverage machine learning algorithms (e.g., classification, clustering, time-series modeling) to predict, detect, and manage anomalies.

• Collaborate with engineers and business teams to integrate models into production systems.

• Conduct performance monitoring, fine-tuning, and validation of ML models to ensure accuracy and reliability.

• Prepare technical documentation, visualizations, and reports to communicate findings effectively to business and technology stakeholders.

Required Skills & Qualifications:

• Bachelor’s or master’s degree in computer science, Data Science, Statistics, Mathematics, or a related field.

• 1+ years of hands-on experience in data science, AI, or ML engineering.

• Strong proficiency in Python, R, or Scala with experience using data science libraries (e.g., NumPy, Pandas, Scikit-learn, PyTorch, TensorFlow).

• Solid understanding of Data Science with a heavy focus on statistical modeling and Machine Learning, hypothesis testing, regression analysis, and variance modeling.

• Experience with anomaly detection techniques — supervised, unsupervised, and hybrid approaches.

• Experience in Generative AI based implementations.

• Expertise in working with large datasets using SQL, Spark, or similar data-processing frameworks.

• Strong problem-solving, analytical thinking, and communication skills.

• Experience in deploying ML models into production environments, ML Ops, preferably on AWS.

Regards,

DetaPent