Happiest Minds Technologies
Website:
happiestminds.com
Job details:
Title Data Scientist
Skills (must have) ? 5+ years of hands-on experience in Data Science,
Machine Learning, or Applied ML.
? Bachelor?s or Master?s 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 skills?writing 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.
Click on Apply to know more.