Data Science Consultant

Qualifications:

• Education: A bachelor’s or master’s degree in computer science, Data Science, Statistics, Mathematics, Engineering, or a related field.
• Experience:
• Minimum of 3 years of experience in data science, analytics, or a related field.
• Proven experience in consulting or client-facing roles is highly desirable.
• Strong portfolio of completed data science projects, preferably with measurable business impact.
• Technical Skills:
• Proficiency in programming languages such as Python
• Solid understanding of machine learning algorithms and techniques
• Experience with data manipulation and analysis using SQL
• Experience with data visualization tools like Tableau or Power BI
• Soft Skills:
• Excellent communication and presentation skills, with the ability to explain complex technical concepts to non-technical stakeholders.
• Strong problem-solving abilities and analytical thinking.
• Client-focused with the ability to build strong relationships and deliver high-quality service.
• Ability to work independently as well as in a collaborative, team-oriented environment.
• Certifications (optional):
• Data Science certifications

Machine learning certifications

Roles & Responsibilities

• Client Collaboration: Engage with clients to understand their business objectives, data needs, and challenges. Provide expert guidance on data-driven decision-making and strategy development.
• Data Analysis: Collect, clean, and analyze structured and unstructured data from various sources. Perform exploratory data analysis (EDA) to identify patterns, trends, and insights.
• Model Development: Develop and deploy machine learning models, statistical models, and algorithms to solve business problems. Use tools such as Python, R, SQL, and machine learning libraries (e.g., Scikit-learn, TensorFlow, PyTorch).
• Data Visualization & Reporting: Create clear, actionable visualizations and dashboards using tools like Tableau, Power BI, or matplotlib to communicate findings to both technical and non-technical stakeholders.
• Problem Solving: Provide innovative solutions to complex business challenges through data-driven methodologies. Continuously assess and improve models and analytics processes to optimize performance.
• Consulting & Training: Lead workshops and training sessions to educate clients on data science techniques and tools. Offer advice on data strategy, infrastructure, and best practices.
• Project Management: Lead and manage multiple client projects, ensuring timely delivery of high-quality solutions. Collaborate with other team members, such as data engineers and business analysts, to meet project goals.
• Research & Development: Stay up-to-date with the latest trends, tools, and technologies in data science and machine learning. Contribute to the development of new methods and solutions that can be applied to future projects