Data Scientist

Key Responsibilities:

1. Client-Facing Role & Leadership:
   • Engage directly with clients, understanding their needs and translating those into actionable data science solutions.
   • Lead teams, guiding them in both technical aspects and in delivering impactful results to clients.
   • Help organizations adopt AI and machine learning technologies to drive innovation.
2. Simplifying Complex Results:
   • Take complex data science results or processes and distill them into clear, simple visualizations.
   • Ensure that business stakeholders can understand the outcomes and implications of the work done by your team.
3. Explaining Complex Concepts:
   • Translate advanced data science and machine learning concepts into digestible and understandable language for non-technical clients.
   • Work to bridge the gap between technical complexity and business needs.
4. Development & Collaboration:
   • Collaborate on code with teams to build AI/ML solutions.
   • Comfortable with modern development tools for collaborative software development (including version control, agile practices, etc.).
   • Bonus points if you have experience in software development or DevOps, which can further enhance the collaboration between data scientists and engineering teams.
5. Advanced Analytics Application:
   • Apply advanced analytics techniques to a variety of business challenges across different industries.
   • Synthesize complex data sets to provide actionable insights and value to clients.
6. Project Management:
   • Manage projects from start to finish, ensuring deadlines, milestones, and client expectations are met.
   • Coordinate between various teams (data scientists, business analysts, developers) to deliver results effectively.

Required Skills & Experience:

1. Technologies:
   • Programming languages: Proficiency in Python is required, as it's the primary language used for data science, machine learning, and analytics work.
   • Experience with other tools like SQL, R, or TensorFlow/PyTorch is a plus.
2. Machine Learning & Data Science Knowledge:
   • Deep understanding of modern machine learning techniques and their mathematical foundations (e.g., supervised/unsupervised learning, neural networks, reinforcement learning).
   • Ability to translate complex machine learning algorithms and statistical models into business value for clients.
   • Knowledge of data wrangling, feature engineering, modeling, and model evaluation.
3. Analytics & Visualization:
   • Significant experience in applying advanced analytics to solve real-world business problems.
   • Data visualization skills to present findings in clear, digestible formats for decision-makers.
   • Tools like Tableau, Power BI, or Python visualization libraries (Matplotlib, Seaborn, Plotly) are often used in this role.
4. Client Communication & Leadership:
   • Strong client-facing skills, with the ability to manage and guide client expectations.
   • Excellent written and verbal communication skills to articulate complex technical topics to non-technical audiences.
   • Ability to lead teams, organize workflows, and manage resources effectively.
5. Project Management:
   • Strong project management experience with a focus on delivering results on time and within scope.
   • Familiarity with agile methodologies, project tracking tools (Jira, Trello, Asana, etc.), and team collaboration techniques.
6. Software Development & DevOps (Bonus):
   • If you have experience in software development or DevOps, it will be a huge asset, as it adds to your ability to work cross-functionally with development teams and ensure your solutions can be deployed and scaled effectively.

Ideal Candidate Characteristics:

• Passion for AI and a desire to transform organizations into data-driven, AI-led businesses.
• Strong in both technical execution and client relationship management.
• Able to lead teams, drive innovation, and adapt to different business contexts with ease.
• Comfortable with collaborative coding and using modern development tools to produce high-quality solutions.