Senior Data Scientist

Job Description

• Strong Python familiarity (hands‑on) for data preparation, modeling, and building ML components.
• SQL skills: joins, window functions, CTEs, query optimization.
• Linear/Logistic Regression, Decision Trees, Random Forest, XGBoost, Light
  
  GBM.
• Support Vector Machines, KNN.
• Model evaluation:
  Precision/Recall, F1, ROC‑AUC, MSE, RMSE.
• Model tuning:
  Grid search, randomized search, cross‑validation.
• Deep Learning:
  Tensor Flow, Keras, PyTorch, CNNs, RNNs, LSTMs, Transformers, use cases in NLP, computer vision, time‑series forecasting.
• Feature engineering: missing data handling, outlier detection, normalization/standardization.
• Data cleaning and data wrangling & preprocessing.
• Data visualisation:
  Matplotlib, Seaborn, Plotly.
• BI Tools:
  Tableau, Power BI.
• Dashboards, reporting, storytelling with data.
• Big Data & Cloud Tools (for production‑scale roles):
  Need experience with Spark, Hadoop, and one cloud platform (AWS, Azure, or GCP).
• Deployment skills:
  Flask, FastAPI, Docker, Kubernetes (optional), CI/CD basics.
• Databases & Data Engineering basics:
  MySQL, Postgre
  
  SQL, SQL Server, Mongo
  
  DB, Cassandra, Airflow, Prefect (optional).

• Define the ML use case, success metrics, and evaluation criteria; liaise with business stakeholders to translate needs into an ML approach.
• Perform data exploration, data quality checks, feature engineering, and dataset preparation for training and testing.
• Build, train, validate, and iterate ML models; compare experiments and select the best candidate model.
• Package the solution for production (e.g., containerised scoring/service endpoint) and support deployment with engineering/MLOps practices.
• Set up basic monitoring of model accuracy/health and support continuous improvement post‑release.

• Solid foundation in ML concepts (supervised/unsupervised, evaluation, validation) and practical experimentation.
• Experience taking models to production in a cloud‑agnostic way (portable design, API/service mindset).
• Working knowledge of version control and basic CI/CD‑style collaboration with engineering teams.
• Bachelor’s degree in Computer Science or related field.

$125,000–$145,000 per year.

• Discretionary annual incentive.
• Comprehensive medical coverage: medical, dental & vision, disability planning & insurance, pet insurance plans.
• Family support: maternal & parental leave.
• Insurance options: auto & home insurance, identity theft protection.
• Convenience & professional growth: commuter benefits & certification & training reimbursement.
• Time off: vacation, time off, sick leave & holidays.
• Legal & financial assistance: legal assistance, 401(k) plan, performance bonus, college fund, student loan refinancing.