Data Science Lead

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🕒 April 1

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Particle41

WebsiteLinkedInAll Job Openings

51 - 200 employees

☁️ SaaS

🤖 Artificial Intelligence

🏢 Enterprise

SaaS • Artificial Intelligence • Enterprise

Particle41 is a company that specializes in providing expertise and solutions in technology development, data science, and DevOps. They offer CTO advisory services, acting as a partner to strengthen tech strategies and deliver robust software solutions tailored to their clients' needs. Particle41 emphasizes modernizing operations using cloud architecture, integrating software systems, and leveraging artificial intelligence to provide innovative digital products. They work closely with businesses to ensure on-time project delivery, scalability, and maintaining data security. Particle41 is particularly focused on helping businesses improve their competitive edge through strategic tech solutions and ongoing support.

📋 Description

• 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.

🎯 Requirements

• 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. • Proficiency in database modeling for OLTP/OLAP systems and expertise with relational and NoSQL databases. • Strong mentoring skills, with a proven track record of guiding junior team members in AI/ML best practices. • Bachelor’s degree in a related field (required), with a Master’s or PhD in Data Science or a related field (preferred). • 7+ years of experience in data science, machine learning, or data engineering. • AWS or Azure certification (strongly preferred). • Strong communication and leadership skills with experience working cross-functionally to deliver high-impact data solutions.

🏖️ Benefits

• Health insurance • Retirement plans • Paid time off • Flexible work arrangements • Professional development