Data Science & Engineering Lead

Data Science & Engineering Lead

Lead the charge in AI and data innovation as our Data Science & Engineering Lead. Here, you’ll work hands-on with our talented team to build and deploy high-impact ML models, optimize our data systems, and drive actionable insights. If you’re skilled in advanced AI tools like Databricks, Spark, and Azure, and excited to push boundaries in machine learning and engineering, we want you on our team. Join us for meaningful work, a collaborative culture, and competitive benefits.

In This Role, You Will:

• Design and implement supervised and unsupervised ML models (e.g., OLS, Logistic Regression, Ensemble Methods) to solve real-world business problems.
• Lead model development for advanced architectures in neural networks, such as ANN, CNN, RNN, GAN, Transformers, and RESNet.
• Drive NLP advancements with tools like NLTK and neural-based language models.
• Oversee the development of computer vision models, utilizing OpenCV for real-world applications.
• Lead time-series modeling projects for forecasting and anomaly detection.
• Utilize AI techniques like Retrieval-Augmented Generation (RAG), Chain of Thought (CoT), and Model of Alignment (MOA) to enhance model performance.
• Build and maintain scalable data pipelines for both streaming and batch processing.
• Architect and optimize lakehouse solutions using Delta/Iceberg and bronze-silver-gold architectures.
• Lead the development of ETL processes with tools like Airflow, DBT, and Airbyte to support data flow and transformation.
• Design and optimize database models for OLTP and OLAP systems using Snowflake, SQL Server, PostgreSQL, and MySQL.
• Develop NoSQL solutions, leveraging MongoDB, DynamoDB, and ElasticSearch for unstructured data.
• Lead efforts in building cloud infrastructure, particularly in AWS (preferred) or Azure, using services such as Lambda, API Gateway, Batch processing, Kinesis, and Kafka.
• Oversee MLOps pipelines for robust deployment of ML models in production with platforms like Sagemaker, Databricks, and Azure ML Studio.
• Develop and optimize business intelligence dashboards with tools like Tableau, QuickSight, and PowerBI for actionable insights.
• Implement GPU acceleration and CUDA for model training and optimization.
• Mentor junior team members in cutting-edge AI/ML techniques and best practices.

Skills and Experience We Value:

• Proven expertise in both supervised and unsupervised ML, advanced deep learning, including TensorFlow, PyTorch, and neural network architectures (e.g., CNN, GAN, Transformers).
• Hands-on experience with machine learning libraries and tools such as Scikit-learn, Pandas, and Numpy.
• Proficiency in AI model development using LLM libraries (e.g., Langchain, Huggingface, OpenAI).
• Strong MLOps skills, with experience deploying scalable pipelines in production using tools like Sagemaker, Databricks, and Azure ML Studio.
• Advanced skills in big data frameworks like Apache Spark, Glue, and EMR for distributed model training.
• Expertise in ETL processes and data pipeline development with tools like Airflow, DBT, and Airbyte.
• Strong knowledge in lakehouse architectures (Delta/Iceberg) and experience with data quality frameworks like Great Expectations.
• Proficiency in cloud platforms (AWS preferred or Azure), with a deep understanding of services like IAM, VPC networking, Lambda, API Gateway, Batch, Kinesis, and Kafka.
• Proficiency with Infrastructure as Code (IaC) tools such as Terraform or CloudFormation for automation.
• Advanced skills in GPU acceleration, CUDA, and distributed model training.
• Demonstrated ability to architect and deploy scalable machine learning and data-intensive systems.