Remote Data Scientist Job in New York, NY Sonatus

Who you are

• MS or PhD in Computer Science, Engineering, Sciences, Applied Mathematics, or a related field
• Strong knowledge and understanding of current ML and AI technologies and platforms
• Knowledge of current technologies and ML frameworks (tensorflow, pytorch, sklearn, keras, etc.)
• Hands-on experience with data engineering/processing frameworks (databricks, spark, dataflow, etc.)
• Data Science prototyping experience
• Results-driven with a positive can-do attitude
• Creative problem-solving skills
• Experience with SQL
• Excellent verbal and written communication skills
• Prefer experience with distributed storage and computing tools such as Hadoop and Spark
• Prefer working knowledge of streaming data solutions

Desirables

• Results-driven
• Positive attitude
• Creative
• Excellent communication skills

What the job involves

• Perform data analysis and offer insights to inform business decisions
• Work collaboratively with cross-functional teams to identify and address intricate data challenges
• Create and deploy data models and algorithms to enhance decision-making processes
• Employ programming languages and tools for extracting, cleansing, and manipulating extensive datasets
• Present findings and recommendations to stakeholders using visualizations and presentations
• Stay current with industry trends and advancements in Data science
• Take charge of projects, ensuring timely completion in a dynamic work environment
• Engage in training and developmental activities to augment skills and knowledge
• Adhere to data privacy and security protocols to uphold the confidentiality of sensitive information
• Perform the full data modeling and algorithm development cycle: modeling, training, tuning, validating, deploying, and maintaining services
• Research and develop novel statistical approaches and machine learning/deep learning models that add value to manufacturing processes and product performance improvement
• Assess the effectiveness and accuracy of data sources and data-gathering techniques