Expert Data Scientist

About the Role

Expert Data Scientist

Responsibilities

• Develop prototype solutions, mathematical models, algorithms, machine learning techniques, and robust analytics to support insight generation and data visualization.
• Conduct exploratory data analysis to uncover trends, patterns, and high-level insights.
• Provide optimization recommendations to support KPIs across product, marketing, operations, PR, and other business units.
• Collaborate with engineering teams to ensure developed solutions meet standards for functionality, scalability, performance, and reliability.
• Work with business analysts and data engineers to understand their use cases and support implementation.
• Identify opportunities for leveraging data to solve business problems and improve outcomes.
• Drive innovation by researching and applying new methods, tools, and statistical techniques to improve decision-making processes.
• Mentor teammates and promote knowledge sharing across the team.
• Participate in community-building activities, internal knowledge exchange, and conferences.
• Support marketing and sales teams through technical input, content creation, and customer meetings.

Requirements

General Technical Requirements

• BSc, MSc, or PhD in Mathematics, Statistics, Computer Science, Engineering, Operations Research, Econometrics, or related fields.
• Strong knowledge of

  probability theory

  ,

  statistics

  , and the mathematics behind

  machine learning

  .
• Experience using

  CRISP-ML(Q)

  or

  TDSP

  methodologies to solve business problems.
• Proficient in machine learning techniques including:
• Regression
• Classification
• Clustering
• Dimensionality reduction
• Strong proficiency in

  Python

  for modeling and statistical analysis.
• Skilled in data visualization with libraries such as

  Matplotlib

  ,

  Seaborn

  , or

  Plotly

  .

Specific Technical Skills

• Advanced SQL skills for data manipulation, sampling, and reporting.
• Experience with:
• Imbalanced datasets
• Time series data (preprocessing, feature engineering, forecasting)
• Outlier and anomaly detection
• Handling various data types (text, image, video)
• Familiarity with cloud-based ML services:

  AWS SageMaker

  ,

  Azure ML

  , or

  Google AI Platform

  .

Domain Experience (Healthcare)

• Analyzing medical signals and images.
• Predictive modeling for outcomes, disease progression, readmissions, and population health risks.
• NLP/text mining on clinical notes, medical literature, or patient-reported data.
• Experience with

  survival analysis

  and

  time-to-event modeling

  .
• Designing and analyzing clinical trials or research studies.
• Causal inference methods (e.g., propensity score matching, instrumental variable techniques).
• Knowledge of healthcare regulations like

  HIPAA

  ,

  GDPR

  , and

  FDA

  compliance.
• Secure handling of healthcare data, including

  de-identification

  and patient consent.
• Familiarity with

  federated learning

  and

  decentralized models

  .
• Understanding of healthcare interoperability standards:

  HL7

  ,

  SNOMED

  ,

  FHIR

  ,

  DICOM

  .
• Ability to work with clinicians, researchers, and policymakers to extract actionable insights.

Good to Have Skills

• MLOps

  experience: integrating ML into production, using

  Docker

  ,

  Kubernetes

  .
• Experience in

  deep learning

  with

  TensorFlow

  or

  PyTorch

  .
• Knowledge of

  LLMs

  and

  Generative AI

  .
• Experience with

  MS SQL Server

  ,

  PostgreSQL

  ,

  Databricks

  ,

  Snowflake

  .
• Familiarity with

  Big Data

  technologies (e.g.,

  Hadoop

  ,

  Apache Spark

  ).
• Experience with

  NoSQL databases

  (e.g.,

  Cassandra

  ,

  Neo4j

  ).

Business-Related Requirements

• Demonstrated success in delivering data science projects that drive measurable business impact.
• Ability to translate business problems into data science use cases and execute them end-to-end.
• Excellent project and time management skills.
• Strong communication and storytelling skills for conveying complex technical concepts to stakeholders.

Desirable

• Published research or peer-reviewed journal articles.
• Recognized achievements in data science competitions (e.g.,

  Kaggle

  ).
• Certifications in cloud-based ML platforms (AWS, Azure, GCP).