Note: The job is a remote job and is open to candidates in USA. Precision Technologies is offering a Data Scientist role as part of their Return-to-Work Program aimed at supporting women re-entering the workforce. The position involves designing and deploying machine learning solutions and requires expertise in data science, statistical modeling, and advanced analytics.
Responsibilities
- Designing, developing, and deploying machine learning, statistical modeling, and advanced analytics solutions to solve complex business problems
- Developing supervised and unsupervised machine learning models, including classification, regression, clustering, recommendation systems, anomaly detection, and predictive analytics
- Data wrangling, exploratory data analysis (EDA), feature selection, data cleansing, and processing structured, semi-structured, and unstructured datasets
- Building and deploying AI/ML models on cloud platforms such as AWS, Azure, or GCP, utilizing services like SageMaker, Azure Machine Learning, Vertex AI, or similar technologies
- Creating interactive dashboards and visualizations using Tableau, Power BI, Matplotlib, Seaborn, Plotly, or other data visualization tools to communicate actionable business insights
- Working with Big Data technologies including Apache Spark, PySpark, Hadoop, Databricks, Snowflake, Kafka, and distributed data processing platforms
- Understanding of statistics, probability, hypothesis testing, feature engineering, model evaluation, A/B testing, and experimental design
- Knowledge of SQL and NoSQL databases, data warehousing concepts, ETL/ELT pipelines, and working with databases such as PostgreSQL, MySQL, Oracle, MongoDB, Cassandra, and DynamoDB
- Experience with MLOps, model deployment, CI/CD pipelines, Git, Docker, Kubernetes, MLflow, and model monitoring for production-grade machine learning applications
- Understanding of REST APIs, data engineering workflows, cloud-native architectures, model optimization, performance tuning, and scalable AI solution development
Skills
- Minimum 5+ years of experience in designing, developing, and deploying machine learning, statistical modeling, and advanced analytics solutions to solve complex business problems
- Expertise in Python, R, SQL, Pandas, NumPy, SciPy, Scikit-learn, TensorFlow, PyTorch, and other data science and machine learning frameworks
- Experience developing supervised and unsupervised machine learning models, including classification, regression, clustering, recommendation systems, anomaly detection, and predictive analytics
- Understanding of statistics, probability, hypothesis testing, feature engineering, model evaluation, A/B testing, and experimental design
- Experience with data wrangling, exploratory data analysis (EDA), feature selection, data cleansing, and processing structured, semi-structured, and unstructured datasets
- Experience working with Big Data technologies including Apache Spark, PySpark, Hadoop, Databricks, Snowflake, Kafka, and distributed data processing platforms
- Experience building and deploying AI/ML models on cloud platforms such as AWS, Azure, or GCP, utilizing services like SageMaker, Azure Machine Learning, Vertex AI, or similar technologies
- Knowledge of SQL and NoSQL databases, data warehousing concepts, ETL/ELT pipelines, and working with databases such as PostgreSQL, MySQL, Oracle, MongoDB, Cassandra, and DynamoDB
- Experience creating interactive dashboards and visualizations using Tableau, Power BI, Matplotlib, Seaborn, Plotly, or other data visualization tools to communicate actionable business insights
- Experience with MLOps, model deployment, CI/CD pipelines, Git, Docker, Kubernetes, MLflow, and model monitoring for production-grade machine learning applications
- Understanding of REST APIs, data engineering workflows, cloud-native architectures, model optimization, performance tuning, and scalable AI solution development
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