Title Data Scientist
Skills (must have) 5+ years of hands-on experience in Data Science,
Machine Learning, or Applied ML.
Bachelors or Masters degree in Computer Science,
Data Science, Statistics, Mathematics, Engineering, or
a related field.
Strong Python programming skills with experience in:
o pandas, NumPy, scikit-learn
o TensorFlow or PyTorch for deep learning projects.
Proven experience designing, training, tuning, and
validating ML models:
o Supervised (classification, regression)
o Unsupervised (clustering, anomaly detection)
o Time-series/forecasting
o Strong expertise in feature engineering, EDA, and
statistical analysis.
Deep understanding of:
o ML algorithms
o Model evaluation techniques
o Probability & statistics
o Linear algebra & optimization fundamentals
Experience working with large datasets using:
o Apache Spark, Dask, Databricks
o Or cloud ML platforms like Azure ML, AWS
SageMaker, GCP Vertex AI
Strong SQL skillswriting optimized, complex queries
involving joins, aggregations, and window functions.
Hands-on experience with MLOps concepts:
o Experiment tracking (MLflow, Weights & Biases)
o Model versioning & registries
o CI/CD workflows for ML
o Reproducibility and testing
Experience deploying models in production using:
o REST APIs
o Docker containers
o Serverless compute (Azure Functions, AWS
Lambda, Cloud Run)
Understanding of Responsible AI concepts:
o Model monitoring
o Fairness & bias evaluation
o Drift detection
o Explainability tools (SHAP, LIME)
Strong data storytelling skills using visualizations:
o Matplotlib, Seaborn, Plotly
o Dashboard tools: Power BI, Tableau
Skills (good to have) Experience with NLP: transformer models,
embeddings, text classification, summarization.
Exposure to LLMs, vector databases (Pinecone,
Weaviate, Redis), and RAG architectures.
Experience with Snowflake Snowpark ML, Databricks
ML, or Azure ML pipelines.
Exposure to feature stores (Feast, Databricks Feature
Store, SageMaker FS).
Container orchestration and microservices: Docker,
Kubernetes.
Experience with advanced methods:
o Anomaly detection
o Recommender systems
o Causal inference or uplift modeling
Experience with experimentation frameworks (A/B
testing, CUPED, DoE).
Key Responsibilities Collaborate with product owners, data engineers,
software engineers, and subject-matter experts to
identify and frame business problems suitable for ML or
statistical modeling.
Explore, clean, and transform raw data into
high-quality datasets for modeling.
Design, build, and validate machine learning models
end-to-end, applying best practices in feature
engineering, experiments, and evaluation.
Build scalable training and inference pipelines in
collaboration with data engineering teams.
Deploy ML models into production, ensuring reliability,
performance, and resilience.
Conduct advanced statistical analysis and develop
dashboards to generate insights for decision-makers.
Monitor model performance, detect drift, diagnose
data issues, and implement retraining or model refresh
cycles.
Apply MLOps best practices, including reproducibility,
automated testing, model lifecycle management, and
CI/CD integration.
Stay current with the latest ML research, evaluate new
techniques, and drive innovation in algorithms,
architectures, and approaches.
Mentor and guide junior data scientists through
technical reviews and knowledge sharing.
Document methodologies, assumptions, modeling
processes, and results clearly for both technical and
non-technical audiences.
Soft Skills & Behavioral
Expectations
Strong analytical thinking and problem-solving skills.
Ability to break down complex ML concepts for
non-technical stakeholders.
Ownership mindset takes initiative and drives
projects independently.
Strong collaboration skills across engineering,
product, and business teams.
Curiosity and commitment to continuous learning
and experimentation.
Ability to balance scientific rigor with practical
business needs.