Job Summary:
We are seeking a highly skilled and motivated Data Scientist with a strong background in Artificial Intelligence (AI) and Machine Learning (ML) to join our growing analytics team. You will be responsible for designing, developing, and deploying data-driven models and AI solutions that solve real-world business problems and drive strategic decision-making.
Key Responsibilities:
- Develop and implement advanced machine learning models, AI algorithms, and statistical models to analyze large, complex datasets.
- Apply data mining techniques and perform predictive and prescriptive analytics.
- Design and conduct experiments (e.g., A/B testing, simulation models) to evaluate model performance and business impact.
- Collaborate with cross-functional teams including engineering, product, and business stakeholders to identify opportunities for leveraging data to drive innovation.
- Build and maintain end-to-end ML pipelines for model training, validation, deployment, and monitoring.
- Interpret and communicate findings and insights to both technical and non-technical audiences.
- Stay current with the latest research and trends in AI/ML and integrate relevant advancements into ongoing projects.
Required Skills & Qualifications:
- Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, or related field.
- 3+ years of experience working in a Data Science or Machine Learning role.
- Proficiency in Python and common libraries (e.g., scikit-learn, pandas, NumPy, TensorFlow, PyTorch).
- Strong background in machine learning, deep learning, natural language processing, or computer vision.
- Experience with model evaluation techniques (ROC, F1-score, confusion matrix, etc.).
- Proficient in querying and working with large datasets using SQL or NoSQL databases.
- Experience with data visualization tools (e.g., matplotlib, seaborn, Power BI, or Tableau).
- Strong communication and storytelling skills with the ability to explain complex concepts.
Preferred Qualifications:
- Ph.D. in a quantitative field (optional but advantageous).
- Experience deploying models into production using Docker, Kubernetes, MLflow, or MLOps tools.
- Familiarity with cloud platforms (AWS, Azure, GCP) and distributed computing frameworks (e.g., Spark).
- Domain experience in [insert domain: finance, healthcare, retail, etc.].
Didn’t find the job appropriate? Report this Job
