Senior Data Scientist - Machine Learning

Senior Data Scientist

Experience :

Role Summary

Key Responsibilities

• Strategic Problem Solving : Partner with product managers and business stakeholders to frame complex business problems as quantitative challenges and define measurable project goals.
• Model Development : Design, develop, and implement advanced statistical models, machine learning algorithms, and predictive analytics solutions across various domains (e.g., recommendation systems, fraud detection, forecasting, customer churn).
• Experimentation & Validation : Lead the design and analysis of A/B tests and other experimental methods to validate the impact and effectiveness of models and product features.
• Data Manipulation : Work with large, complex datasets, performing data cleaning, feature engineering, and data analysis using SQL and Python.
• Deployment and Production : Collaborate closely with MLOps and Engineering teams to ensure models are correctly deployed, monitored, and maintained in a production environment, ensuring scalability and reliability.
• Mentorship and Leadership : Provide technical guidance, perform code reviews, and mentor junior and mid-level data scientists, fostering a culture of best practices and innovation.
• Communication : Clearly communicate complex findings, methodologies, and business implications to both technical and non-technical audiences.
  

Required Skills and Qualifications :

• Programming & Tools : Expert proficiency in Python and its data science ecosystem (Pandas, NumPy, Scikit-learn, Statsmodels). Strong expertise in SQL for data extraction and manipulation.
• Machine Learning : Deep, practical knowledge of a wide range of ML techniques (e.g., Regression, Classification, Clustering, Time Series Analysis) and experience with modern deep learning frameworks (TensorFlow or PyTorch).
• Statistics : Solid foundation in statistical inference, hypothesis testing, experimental design (A/B testing), and casual inference methods.
• Cloud & Big Data : Experience working with cloud platforms (AWS, Azure, or GCP) and big data technologies (e.g., Spark, Hive).
• Communication : Excellent ability to distill complex analytical results into clear, concise, and actionable business insights.
• Domain Expertise : Proven track record of applying data science to solve real-world problems in domains such as [Specify relevant domains, e.g., e-commerce, finance, healthcare, operations].
  

Preferred (Bonus) Skills

• Experience with MLOps principles, tools (MLflow, Kubeflow, SageMaker), and the full model lifecycle deployment.
• Advanced knowledge of Natural Language Processing (NLP) or Computer Vision (CV) techniques.
• Familiarity with data visualization tools (Tableau, Power BI, or Matplotlib/Seaborn).
• Experience in a production-focused or high-throughput environment.