Role Overview
The Data Scientist / ML Engineer will play a key role in enhancing our automation services with predictive, adaptive, and data-driven solutions. This resource will work closely with automation engineers, architects, and clients to analyze legacy systems, identify opportunities for intelligent automation, and develop machine learning models that improve modernization outcomes.
Key Responsibilities
- Analyze legacy system data, workflows, and logs to identify automation opportunities and predict process bottlenecks.
- Develop and deploy ML models to enhance automation solutions, including intelligent process handling, anomaly detection, and predictive monitoring.
- Apply natural language processing (NLP) and code analysis techniques to support modernization of legacy applications.
- Design and implement data cleansing, migration, and validation models to ensure accuracy during system transitions.
- Collaborate with automation teams to integrate ML models into CI/CD pipelines, automation platforms, and client applications.
- Create dashboards, reports, and insights to communicate findings and business impact.
- Stay current with ML/AI advancements and recommend new approaches to improve automation solutions.
Qualifications
- Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or related field.
- 3+ years of experience in data science, machine learning, or applied AI.
- Proficiency in Python or R, SQL, and ML frameworks (scikit-learn, TensorFlow, PyTorch).
- Experience with data preprocessing, feature engineering, and model deployment.
- Knowledge of automation frameworks, RPA tools, or legacy application environments is a strong plus.
- Strong problem solving skills and ability to translate technical models into business value.
- Excellent communication skills for working with technical teams and non-technical stakeholders.
