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Data Analytics Platform Developer

2 weeks ago

Hong Kong Island, Hong Kong SAR China Intellectsoft Full time
About Intellectsoft
We are a software development company delivering innovative solutions since 2007. We operate across North America, Latin America, the Nordic region, the UK, and Europe. Our mission is to partner with startups, mid-sized businesses, and Fortune 500 companies to drive growth and scalability. We specialize in industries like Fintech, Healthcare, EdTech, Construction, Hospitality, and more, partnering with our clients to deliver solutions that make a difference. Learn more at www.intellectsoft.net

Our Team
  • We have a strong team of data scientists, engineers, and business analysts who work together to deliver innovative solutions.
  • Our team members have extensive experience in data science, machine learning, and statistical modeling.
  • We use cutting-edge technologies like MLflow, PyTorch, Spark MLlib, and FastAPI for model development and deployment.
  • We have a strong understanding of distributed computing and big data processing using Apache Spark and ClickHouse.
  • We are proficient in feature engineering, data preprocessing, and model tuning for large-scale datasets.
  • We have experience in building and deploying ML models in production environments using TorchServe, FastAPI, or similar frameworks.
  • We have knowledge of deep learning architectures (CNNs, RNNs, transformers) and their practical applications.
  • We have a strong grasp of MLOps best practices, including CI/CD for ML models, model monitoring, and retraining pipelines.
  • We understand real-time analytics and event-driven architectures for processing streaming data.
  • We have experience working with SQL and NoSQL databases such as PostgreSQL, ClickHouse, and Delta Lake.
  • We have a strong ability to collaborate with data engineers, architects, and business analysts to ensure ML models align with business objectives.
  • We have knowledge of A/B testing methodologies and causal inference techniques for evaluating model effectiveness.
  • We are familiar with cloud services (AWS, GCP, or Azure) for scalable model training and deployment.