Data Scientist

Role & responsibilities

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As a Senior Data Scientist in Analytics, you will leverage machine learning algorithms and advanced analytics to uncover actionable insights from large and complex datasets. You will work closely with product, engineering, and business teams to create data-driven solutions that enhance decision-making, optimize operations, and support business growth. This role offers an exciting opportunity to contribute to cutting-edge machine learning projects while mentoring a team of junior data scientists and analysts.

Key Responsibilities:

• Machine Learning Model Development: Design, develop, and deploy machine learning models for predictive analytics, classification, regression, and anomaly detection. Implement advanced algorithms such as deep learning, ensemble methods, and reinforcement learning to solve business problems.
• Data Analysis & Feature Engineering: Analyze large, complex datasets to extract valuable insights. Perform data preprocessing, cleaning, and feature engineering to enhance the accuracy and performance of machine learning models.
• Collaborate with Cross-Functional Teams: Work closely with product managers, engineers, and business stakeholders to identify business needs, develop solutions, and implement models that address these needs.
• Advanced Analytics: Apply statistical analysis and machine learning techniques to develop predictive models and deliver data-driven insights that directly influence business strategies and operations.
• Model Evaluation & Tuning: Regularly evaluate model performance, conduct A/B testing, and optimize model parameters to ensure the highest quality and business alignment.
• Scalability & Deployment: Lead the deployment of machine learning models into production systems, ensuring models are scalable, reliable, and maintainable.
• Mentorship & Leadership: Provide technical guidance and mentorship to junior data scientists, sharing best practices and helping them grow their skills in machine learning and data analytics.
• Stay on the Cutting Edge: Stay current with advancements in machine learning, AI, and data science to continuously improve our analytics capabilities and maintain a competitive edge.
• Reporting & Visualization: Create data visualizations and reports that clearly communicate the outcomes of your models and insights to non-technical stakeholders.

Required Qualifications:

• Education: Bachelor's in Computer Science, Data Science, Mathematics, Statistics, or a related field.

• Experience:

• Minimum of 5+ years of hands-on experience in data science, machine learning, and advanced analytics.

• Strong background in developing machine learning models and applying them to business problems.
• Experience working with large datasets and distributed computing environments.
• Proficiency in programming languages such as Python, R, and SQL for data analysis and modeling.

• Expertise in machine learning frameworks such as TensorFlow, PyTorch, Scikit-learn, XGBoost, or similar.

• Technical Skills:

• Proficient in building, training, and validating machine learning models.

• Expert in Sql, NL, DS, ML.
• Expertise in statistical analysis, hypothesis testing, and feature engineering.
• Experience with data manipulation and processing libraries (e.g., Pandas, NumPy).

• Knowledge of cloud platforms (e.g., AWS, GCP, Azure) and tools for deploying machine learning models.

• Business Acumen: Ability to collaborate with cross-functional teams to solve business problems through data and machine learning solutions.

• Communication Skills: Strong ability to communicate complex technical concepts to non-technical stakeholders and business leaders.

Preferred Qualifications:

• Advanced ML Techniques: Experience with deep learning (e.g., CNNs, RNNs), natural language processing (NLP), and reinforcement learning is a plus.
• Big Data Technologies: Familiarity with big data tools and technologies such as Hadoop, Spark, or Kafka.
• Cloud Deployment: Experience deploying models using cloud-based machine learning platforms or containerized environments (e.g., Docker, Kubernetes).
• Experience in Business Analytics: Exposure to working in a specific industry (e.g., healthcare, finance, e-commerce) and understanding the domain's unique challenges.

Why Join US:

• Competitive salary and comprehensive benefits package.
• Flexible work hours and remote work options.
• Opportunity to work with cutting-edge technologies and innovative projects.
• Collaborative, inclusive, and supportive team culture.
• Professional development and growth opportunities.