Remote Data Science & Engineering Lead

Description:

• The Data Science & Engineering Lead will spearhead AI and data innovation within the organization.
• This role involves hands-on collaboration with a talented team to build and deploy impactful machine learning models.
• Responsibilities include designing and implementing supervised and unsupervised ML models to address real-world business challenges.
• The lead will oversee model development for advanced neural network architectures, including ANN, CNN, RNN, GAN, Transformers, and RESNet.
• The position requires driving advancements in natural language processing using tools like NLTK and neural-based language models.
• The lead will also manage the development of computer vision models utilizing OpenCV for practical applications.
• Time-series modeling projects for forecasting and anomaly detection will be part of the responsibilities.
• The role includes utilizing AI techniques such as Retrieval-Augmented Generation (RAG), Chain of Thought (CoT), and Model of Alignment (MOA) to improve model performance.
• Building and maintaining scalable data pipelines for both streaming and batch processing is essential.
• The lead will architect and optimize lakehouse solutions using Delta/Iceberg and bronze-silver-gold architectures.
• Development of ETL processes with tools like Airflow, DBT, and Airbyte to support data flow and transformation is required.
• The position involves designing and optimizing database models for OLTP and OLAP systems using Snowflake, SQL Server, PostgreSQL, and MySQL.
• Developing NoSQL solutions with MongoDB, DynamoDB, and ElasticSearch for unstructured data is also part of the role.
• The lead will build cloud infrastructure, particularly in AWS or Azure, using services such as Lambda, API Gateway, Batch processing, Kinesis, and Kafka.
• Overseeing MLOps pipelines for the robust deployment of ML models in production with platforms like Sagemaker, Databricks, and Azure ML Studio is necessary.
• The role includes developing and optimizing business intelligence dashboards with tools like Tableau, QuickSight, and PowerBI for actionable insights.
• Implementing GPU acceleration and CUDA for model training and optimization is required.
• Mentoring junior team members in cutting-edge AI/ML techniques and best practices is an important aspect of the role.

Requirements:

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

Benefits:

• The position offers meaningful work within a collaborative culture.
• Competitive benefits are provided to all employees.
• Particle41 values dependability, ensuring exceptional results for clients.
• The company promotes a diverse and inclusive work environment, welcoming individuals from all backgrounds.
• Equal employment opportunities are guaranteed, with hiring decisions based on merit and qualifications without discrimination.