Applied Data Scientist / Machine Learning Engineer (Decision Intelligence) | JobSetuu

Description

Job Overview

• Source: Jobicy

Job Description

We are looking for a product-minded Applied Data Scientist or Machine Learning Engineer to help build, ship, and scale ML-powered products that directly improve how our customers make decisions, operate their businesses, and serve their own users.

This is not a research-only role, nor is it a service-oriented internal analytics position. We want someone who has taken machine learning from problem definition through experimentation, production deployment, measurement, iteration, and long-term ownership. You understand that great models are not just accurate in notebooks—they are usable, explainable, measurable, scalable, and valuable inside a real product.

Whether your background leans heavily toward Data Engineering/ML Ops or Applied Data Science, you have a strong bias toward shipping and an interest in bridging both worlds to bring AI to life.

WHAT YOU'LL DO:

Engineering & AI Enablement

End-to-End ML Ownership: Drive the development of machine learning capabilities (forecasting, recommendation, ranking, optimization, or decision intelligence) powering customer-facing SaaS products.

Pipeline & Model Development: Design reliable data and feature pipelines alongside models from discovery through experimentation, validation, deployment, and monitoring.

Product Integration: Partner with Product Managers and Software Engineers to embed ML directly into product workflows, user experiences, and decision-making tools.

Pragmatic Prototyping: Move quickly from prototype to production while balancing accuracy, interpretability, latency, maintainability, and business impact.

Ecosystem Ownership & Strategy

Evaluation & Experimentation: Define offline and online evaluation strategies, including model quality, drift, and reliability. Design A/B tests and causal measurement frameworks to prove ML features improve customer outcomes.

Data Health & Feedback Loops: Collaborate with Data teams to ensure models are supported by high-quality features, while building feedback loops so product experiences improve over time.

Platform & MLOps Support: Help manage and optimize cloud data infrastructure, ensuring trustworthy insights and proactively managing data health before it impacts users.

Product & Technical Direction

Strategic Judgment: Bring strong judgment around when to use traditional ML, statistical modeling, LLMs, heuristics, or simpler product logic. Make practical trade-offs across model complexity and customer impact.

Roadmap Influence: Clearly communicate what ML can and cannot solve to influence roadmap decisions, helping identify where machine learning can create true product differentiation.

Mentorship: Guide and mentor other data scientists, ML engineers, analysts, and cross-functional partners in applied ML best practices.

WHO YOU ARE:

The Proven Builder: You have shipped ML into real products. You are comfortable starting with an ambiguous product problem, figuring out if ML is the right solution, building it, and measuring whether it worked.

Product-First Architect: You care about product impact as much as model performance. You know that a model with slightly lower accuracy but higher trust, faster inference, better explainability, and stronger user adoption is the better product decision.

A Multi-Disciplinary Executioner: You understand that a model is only as good as the pipeline feeding it. You prioritize usability, "Time to Insight," and customer trust as much as you do code efficiency.

WHAT YOU’LL BRING:

• Experience: 3+ years (ideally 5+) of professional experience in applied data science, machine learning, or ML engineering, including hands-on experience building and shipping models into production products. Experience with SaaS products is highly valued.

• Technical Core: Strong Python skills and hands-on experience with applied ML libraries and frameworks (e.g., Scikit-Learn, XGBoost, PyTorch, TensorFlow). Solid SQL expertise is required.

• ML & Modeling Depth: Strong understanding of supervised learning, forecasting, ranking, recommendation systems, optimization, or statistical modeling. Experience with real-world, imperfect product datasets is essential.

• Ops & Orchestration: Familiarity with MLOps concepts (model versioning, feature pipelines, orchestration via Airflow/dbt/Dagster, monitoring, drift detection) and modern data platforms (e.g., Snowflake, BigQuery, Redshift, Databricks).

• Cloud Infrastructure: Hands-on experience operating within cloud environments (AWS, GCP, or Azure).

• Communication & Collaboration: Excellent communication skills with the ability to explain complex technical trade-offs clearly to product, engineering, and non-technical business stakeholders.

BONUS POINTS FOR:

• Experience with decision intelligence, forecasting, customer behavior modeling, workforce/route optimization, or operational intelligence products.

• Experience with LLMs, GenAI, or agentic workflows applied to real product use cases.

• Prior experience acting as a Senior or Lead scientist responsible for guiding technical direction.

Expert Career Tips for Applied Data Scientist / Machine Learning Engineer (Decision Intelligence) Roles

To succeed in a competitive market as a Applied Data Scientist / Machine Learning Engineer (Decision Intelligence), you need more than just technical skills. Here are some expert strategies to elevate your profile:

• Build a Strong Portfolio: For technical roles, a clean GitHub or a personal project site is essential. For non-technical roles, a case study portfolio demonstrating problem-solving and impact is equally valuable. Show, don't just tell, what you have achieved in your previous positions.
• Master the Narrative: When interviewing, use the STAR method (Situation, Task, Action, Result) to structure your answers. Quantify your results wherever possible—mentioning "increased efficiency by 20%" is much more impactful than saying "improved efficiency."
• Continuous Learning: The industry moves fast. Whether it's staying updated with the latest AI tools or mastering a new management methodology, continuous professional development is key. Consider obtaining industry-recognized certifications that align with Applied Data Scientist / Machine Learning Engineer (Decision Intelligence) requirements.
• Networking: Connect with other professionals in similar roles. Join online communities, attend webinars, and engage in meaningful discussions on professional social networks. Often, the best opportunities come through referrals and community engagement.
• Soft Skills Matter: Communication, empathy, and leadership are often the deciding factors between two equally qualified technical candidates. Cultivate these skills as they are universally valued across all industries and seniority levels.

Additionally, research the specific company's culture and values. Tailoring your application to show how you align with their mission can significantly increase your chances of moving forward in the process.

Salary & Compensation

Salary not disclosed; typically competitive for the role.

Work Arrangement

Type: On-Site

Standard business hours at the office.

Comprehensive Application Strategy & Hiring Process

Applying for a new role is a marathon, not a sprint. Follow this strategic approach to maximize your success rate:

1. Initial Research & Tailoring

Don't send the same resume to every employer. Spend at least 30 minutes researching the company. Look for recent news, their product roadmap, and their team structure. Modify your summary and core competencies to reflect the specific keywords found in the job description.

2. The Perfect Cover Letter

If the application allows for a cover letter, use it to tell a story that your resume cannot. Explain why you are passionate about this specific company and how your unique background makes you the perfect fit for the challenges they are currently facing.

3. Navigating the Multi-Stage Interview

Most modern hiring processes involve 3-5 stages. This typically includes a recruiter screen, a technical or skill-based assessment, a peer interview, and a final leadership round. Prepare for each stage differently: focus on enthusiasm and fit for the recruiter, technical depth for the assessment, and strategic vision for the leadership round.

4. Post-Interview Follow-Up

Always send a personalized thank-you note within 24 hours of each interview. Reference a specific topic discussed during the call to demonstrate your active listening and genuine interest in the role.

By following these steps, you demonstrate a high level of professionalism and attention to detail that sets you apart from the average applicant.

Typical Interview Process

1. Resume screening
2. HR call
3. Skill interview
4. Final manager interview
5. Offer

Tip: Research the company's products and culture.

Global Market Intelligence & Relocation Insights

At JobSetuu, we specialize in helping talent navigate the global job market. Here is what you need to know about the current landscape in Global and beyond:

The demand for skilled professionals is increasingly borderless. For roles based in Global, understanding the local cost of living, visa requirements (if applicable), and cultural nuances is vital. If this is a remote role, consider the time zone alignment and the asynchronous communication culture of the hiring organization.

Relocation Support: Many forward-thinking companies offer relocation packages that include moving stipends, temporary housing, and legal assistance with work permits. When evaluating an offer, look beyond the base salary—consider the total compensation package, including equity, bonuses, and healthcare benefits.

Work-Life Balance Trends: Hybrid and remote work have become standard in many regions. Research the local labor laws and common practices regarding work hours and vacation time to ensure the role aligns with your lifestyle goals.

Leveraging JobSetuu's tools can help you compare salaries across different cities and understand the "purchasing power" of your potential offer, ensuring you make an informed decision for your long-term career path.

Skills & Competency Roadmap for Professional Development

To remain competitive in Professional Development, we recommend focusing on the following core competencies over the next 12-18 months:

• Technical Mastery: Deepen your expertise in the core tools and languages relevant to your field. For developers, this might be cloud architecture; for marketers, it might be data-driven attribution modeling.
• AI Augmentation: Learn how to leverage generative AI and automation tools to increase your productivity. Understanding how to integrate these technologies into your workflow is becoming a non-negotiable skill.
• Leadership & Strategy: Even in individual contributor roles, the ability to think strategically and lead projects from inception to completion is highly valued. Focus on stakeholder management and high-level project planning.
• Data Literacy: The ability to interpret data and use it to drive decisions is essential across all business functions. Familiarize yourself with data visualization and basic analytical concepts.

By investing in these areas, you not only prepare yourself for the role you are applying for today but also build a resilient foundation for the opportunities of tomorrow.

Apply via JobSetuu

Discover your next career milestone on JobSetuu. This Applied Data Scientist / Machine Learning Engineer (Decision Intelligence) position is part of our commitment to bringing you the most relevant and high-impact job openings globally. At JobSetuu, we simplify your job search by aggregating premier listings and providing the tools you need to stand out. Don't miss the chance to elevate your professional journey—explore more opportunities and career insights on our platform today.