Data Science and Engineering Lead

October 26

Apply Now

Receive Emails with Similar Jobs

Particle41

WebsiteLinkedInAll Job Openings

Application Development • DevOps • Data Science

51 - 200

Description

• Lead the charge in AI and data innovation as our Data Science & Engineering Lead. • Work hands-on with a talented team to build and deploy high-impact ML models. • Optimize our data systems and drive actionable insights. • 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. • 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.

Requirements

• 7+ years of experience in data science, machine learning, or data engineering. • AWS or Azure certification (strongly preferred). • Bachelor’s degree in a related field (required), with a Master’s or PhD in Data Science or a related field (preferred). • Strong communication and leadership skills with experience working cross-functionally to deliver high-impact data solutions. • Strong mentoring skills, with a proven track record of guiding junior team members in AI/ML best practices. • Proficiency in database modeling for OLTP/OLAP systems and expertise with relational and NoSQL databases. • Advanced skills in GPU acceleration, CUDA, and distributed model training. • Demonstrated ability to architect and deploy scalable machine learning and data-intensive systems. • Proficiency with Infrastructure as Code (IaC) tools such as Terraform or CloudFormation for automation. • 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. • Strong knowledge in lakehouse architectures (Delta/Iceberg) and experience with data quality frameworks like Great Expectations. • Expertise in ETL processes and data pipeline development with tools like Airflow, DBT, and Airbyte. • Advanced skills in big data frameworks like Apache Spark, Glue, and EMR for distributed model training. • Strong MLOps skills, with experience deploying scalable pipelines in production using tools like Sagemaker, Databricks, and Azure ML Studio. • Proficiency in AI model development using LLM libraries (e.g., Langchain, Huggingface, OpenAI). • Hands-on experience with machine learning libraries and tools such as Scikit-learn, Pandas, and Numpy. • Proven expertise in both supervised and unsupervised ML, advanced deep learning, including TensorFlow, PyTorch, and neural network architectures (e.g., CNN, GAN, Transformers).