Data Scientist (Machine Learning)

As a Data Scientist, you will play a key role in bridging data science with business value by delivering high-impact solutions across diverse industries. You will work end-to-end-from understanding business requirements to developing models, evaluating outcomes, and deploying scalable solutions-while collaborating with cross-functional teams in a fast-paced, innovation-driven environment.

KEY RESPONSIBILITIESDiscovery and Strategy to Frame Technical Feasibility

• Solution Architecture & Design: Design robust, scalable, and innovative data science solutions while considering performance, maintainability, and business impact.
• Technical Leadership: Provide technical leadership on complex projects, oversee the work of junior and mid-level data scientists, and ensure best practices are followed.
• End-to-End Project Execution: Take ownership of data science projects and modules, from data collection and preparation to model development, evaluation, and MLOps.
• Communication & Storytelling: Effectively communicate complex analytical concepts and findings to both technical and non-technical stakeholders, translating insights into actionable business recommendations.
• Cross-functional Collaboration: Work closely with project managers, engineers, and business stakeholders to align data science efforts with business objectives.
• Stakeholder Management: Engage with senior stakeholders across the organization to understand business needs, present complex findings, and influence data-driven decision-making at a strategic level.
• Innovation & Research: Stay abreast of the latest advancements in data science, machine learning, and AI, exploring opportunities to integrate new technologies and methodologies.
• Mentorship & Coaching: Actively mentor and coach Data Scientists at all levels, fostering a culture of continuous learning and growth within the team.

• Education: Bachelor’s degree in Mathematics, Statistics, Data Science, Computer Science, AI, Engineering, or a highly related field is required. A Master’s degree in any of the above disciplines is preferred.
• Experience: Possess at least 5 years of experience in one or more data science domains such as machine learning, deep learning, natural language processing (NLP), computer vision (CV), optimization, or statistics.
• Technical Proficiency
• Excellence in problem-solving, with the ability to formulate business problems into technical requirements in a scientifically rigorous and effective manner.
• Proven experience leading data science workstreams or projects, and mentoring junior data scientists.
• Fluent in English with strong communication skills, especially when engaging with non-technical business stakeholders.
• Expert in Python and SQL along with relevant data science libraries (Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, etc.) and the ability to write production-ready code.
• Advanced skills in version control (e.g. GitHub, GitLab, etc.) and data visualization.
• Experience with MLOps tools and practices (e.g., pretraining, model tuning, model packaging, CI/CD for ML, model monitoring, ML pipelines, etc.).
• Hands-on experience with cloud platforms (e.g., GCP, AWS, Azure) and containerization (e.g., Docker, Kubernetes).
• Capable of researching, reading publications, and translating research papers into functional code.
• Strong proactive mindset and the ability to take full responsibility for project outcomes.
• Ability to lead the communication of technical concepts and collaborate effectively with cross-functional teams.
• Ability to engage with business stakeholders to gather domain knowledge and requirements, and to communicate timelines, results, and other relevant matters.
• Familiarity with agile delivery practices is a plus.

• Flexible Hours: 5 working days/week (Mon-Fri) with a focus on output, not clock-watching.
• Hybrid Model: 2 days WFH per week (Monday & Friday).
• Generous Leave: 12 days annual leave + special seniority benefits.
• Allowances: Lunch and gasoline support.
• Competitive Income: 13th-month salary + Performance-based bonuses.
• Full Insurance: Social, Health, and Unemployment insurance based on Gross Salary.
• Premium Care: Health Insurance for you, with seniority-based extension to family members.
• Learning Culture: Training for specialized skills, soft skills, and English.
• Engagement: Monthly Happy Hours, Company Trips, and vibrant Sport Clubs (Soccer, Yoga, Badminton).
• Well-being: Annual health check-ups and a supportive, collaborative environment.
• Clear career progression in venturing into domain specific expertise with AI and business knowledge
• Variety of choices of clients for you to interact with to grow and learn.