Ascentt is building cutting-edge data analytics & AI/ML solutions for global automotive and manufacturing leaders. We turn enterprise data into real-time decisions using advanced machine learning and GenAI. Our team solves hard engineering problems at scale, with real-world industry impact. We’re hiring passionate builders to shape the future of industrial intelligence.
Requirements
- Strong programming skills in Python with experience in ML libraries (e.g., scikit-learn, XGBoost, TensorFlow, PyTorch).
- Hands-on experience with PySpark for big data processing and model development.
- Proficient in building models on large-scale datasets (terabytes to petabytes).
- Solid understanding of statistical analysis, probability, hypothesis testing, and experimental design.
- Experience with Amazon SageMaker (or similar cloud-based ML platforms).
- Strong knowledge of ML Ops practices including version control, model monitoring, and retraining strategies.
- Familiarity with containerization (Docker) and CI/CD practices for ML projects is a plus.
Responsibilities
- Design, develop, and deploy scalable machine learning models for real-world business problems using structured and unstructured data.
- Analyze large datasets using PySpark and other distributed computing frameworks to extract insights and prepare features for ML pipelines.
- Apply a wide range of statistical, machine learning, and deep learning techniques, including but not limited to regression, classification, clustering, time-series forecasting, and NLP.
- Own end-to-end ML pipelines from data ingestion, preprocessing, training, validation, tuning, and deployment.
- Utilize Amazon SageMaker or similar platforms for building, training, and deploying models in a production-grade environment.
- Collaborate closely with data engineers, data scientists, and product teams to integrate models with business workflows.
- Monitor and improve model performance, scalability, and reliability in production.
Other
- 7+ years of experience in machine learning, data science, or related fields.
- Excellent communication skills and the ability to clearly explain complex concepts to non-technical stakeholders.
- Master's or Ph.D. in Computer Science, Statistics, Mathematics, or a related quantitative discipline.
- Experience with workflow orchestration tools (e.g., Airflow, Kubeflow).
- Prior experience in domains like Manufacturing, finance, healthcare, or e-commerce is a plus.
