Senior Data Scientist / Senior Machine Learning Engineer

Job Summary:

We are looking for a Senior Data Scientist / Senior ML Engineer to lead the design, development, and deployment of advanced machine learning and AI solutions for our fast-growing organization. In this role, you will work on end-to-end ML pipelines, from problem framing and data exploration to production deployment and monitoring of models at scale.

You will collaborate with product, engineering, and business stakeholders to transform data into actionable insights, improve decision-making, and create AI-driven features that directly impact millions of users. This is an exciting opportunity for a senior professional to own high-impact projects, mentor junior team members, and shape the AI/ML roadmap for the company.

Key Responsibilities:

• Own and lead the full lifecycle of data science projects: problem definition, data collection, model development, validation, deployment, and monitoring.
• Design and implement scalable machine learning models for classification, regression, recommendation, NLP, or computer vision use cases.
• Develop feature engineering pipelines, optimize training workflows, and ensure reproducibility of experiments.
• Work closely with data engineers to improve data quality, build ETL pipelines, and ensure clean and reliable datasets.
• Deploy models into production using MLOps best practices, monitor their performance, and retrain as needed.
• Collaborate with product managers and business leaders to translate business problems into ML solutions.
• Conduct code reviews, design reviews, and mentor junior data scientists/ML engineers to improve team quality.
• Stay updated with the latest developments in AI, ML frameworks, and tools to bring innovative solutions into production.

Required Skills:

• Strong expertise in machine learning algorithms (supervised, unsupervised, deep learning, reinforcement learning).
• Proficiency in Python and key ML libraries/frameworks: Scikit-learn, TensorFlow, PyTorch, XGBoost, LightGBM.
• Experience with data wrangling and feature engineering using pandas, NumPy, or Spark.
• Solid understanding of ML model evaluation, hyperparameter tuning, and experiment tracking (MLflow, Weights & Biases).
• Strong programming skills with production-grade coding practices (modular code, testing, version control with Git).
• Knowledge of MLOps tools and practices – Docker, Kubernetes, CI/CD pipelines for ML, model monitoring.
• Experience working with large datasets, distributed training, and cloud ML services (AWS SageMaker, GCP Vertex AI, Azure ML).
• Excellent understanding of mathematics, statistics, and probability behind ML algorithms.

Experience Range:

5–10+ years of experience in Data Science, Machine Learning, or related fields, with a proven record of delivering production-grade ML solutions.

Preferred Qualifications (Optional):

• Experience with big data ecosystems (Spark, Hadoop, Databricks).
• Expertise in NLP (transformers, LLMs, embeddings) or computer vision (CNNs, YOLO, Detectron2).
• Familiarity with reinforcement learning, generative AI, or large-scale recommendation systems.
• Experience leading AI research initiatives or innovation projects.
• Contributions to open-source ML frameworks or published research papers.

Soft Skills:

• Strong leadership and mentoring abilities – able to guide junior engineers and data scientists.
• Excellent communication skills to explain technical concepts to non-technical stakeholders.
• Strategic thinker who can prioritize initiatives for maximum business impact.
• Ability to work in cross-functional teams and handle ambiguity in problem definitions.
• Passion for continuous learning and pushing boundaries of applied machine learning.

Benefits & Perks:

• Competitive salary package and performance-based bonuses.
• Flexible work arrangements – onsite, hybrid, or remote (as per company policy).
• Health insurance, wellness benefits, and paid time off.
• Access to cutting-edge tools, cloud infrastructure, and datasets.
• Learning budget for certifications, conferences, and workshops.
• Opportunity to work on high-impact AI products that reach millions of users.
• Clear career path toward Principal Data Scientist / AI Architect / Head of Data Science.

Location:

Onsite / Hybrid / Remote (depending on company and project requirements).

Compensation:

Highly competitive and commensurate with experience and seniority. Includes salary + performance incentives + potential equity options for exceptional candidates.