Data Scientist

Job Title: Data Scientist Location: Remote Job Type: Full-Time | Permanent Experience Required: 4+ Years About the Role: We are looking for a highly motivated and analytical Data Scientist with 4 years of industry experience to join our data team. The ideal candidate will have a strong background in Python , SQL , and experience deploying machine learning models using AWS SageMaker . You will be responsible for solving complex business problems with data-driven solutions, developing models, and helping scale machine learning systems into production environments. Key Responsibilities: Model Development: Design, develop, and validate machine learning models for classification, regression, and clustering tasks. Work with structured and unstructured data to extract actionable insights and drive business outcomes. Deployment & MLOps: Deploy machine learning models using AWS SageMaker , including model training, tuning, hosting, and monitoring. Build reusable pipelines for model deployment, automation, and performance tracking. Data Exploration & Feature Engineering: Perform data wrangling, preprocessing, and feature engineering using Python and SQL . Conduct EDA (exploratory data analysis) to identify patterns and anomalies. Collaboration: Work closely with data engineers, product managers, and business stakeholders to define data problems and deliver scalable solutions. Present model results and insights to both technical and non-technical audiences. Continuous Improvement: Stay updated on the latest advancements in machine learning, AI, and cloud technologies. Suggest and implement best practices for experimentation, model governance, and documentation. Required Skills & Qualifications: Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or related field. 4+ years of hands-on experience in data science, machine learning, or applied AI roles. Proficiency in Python for data analysis, model development, and scripting. Strong SQL skills for querying and manipulating large datasets. Hands-on experience with AWS SageMaker , including model training, deployment, and monitoring. Solid understanding of machine learning algorithms and techniques (supervised/unsupervised). Familiarity with libraries such as Pandas, NumPy, Scikit-learn, Matplotlib, and Seaborn. Preferred Qualifications (Nice to Have): Experience with MLOps tools (e.g., MLflow, SageMaker Pipelines). Exposure to deep learning frameworks like TensorFlow or PyTorch. Knowledge of AWS data ecosystem (e.g., S3, Redshift, Athena). Experience in A/B testing or statistical experimentation Show more Show less