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Job Title:
Financial Analytics Data Scientist
Job Requisition ID:
1914
Posting Start Date:
8/6/26
At Daiichi Sankyo, we are united by a single purpose, to improve lives around the world through innovative medicines. With a legacy of innovation since 1899, a presence in more than 30 countries, and more than 19,000 employees, we are advancing breakthrough therapies in oncology, cardiovascular disease, rare diseases, and immune disorders. Guided by our 2030 vision to "be an innovative global healthcare company contributing to the sustainable development of society", we are shaping a healthier, more hopeful future for patients, their families, and society.
Job Summary
Creating measurable business impact through data, analytics and AI while accelerating innovation and enabling smarter decisions at global scale is at the heart of Data Insights, a newly established department within GloBuS. As one of its new capabilities, Financial Analytics will play an essential role in shaping the future of GloBuS through advanced analytics products and AI\-enabled solutions. This is an opportunity to join at the beginning, help shape a new capability and make a lasting impact. The Financial Analytics Data Scientist is a key enabler of robust data\-driven decisions that lead to better financial outcomes for the business. The role develops advanced analytics and AI solutions to solve complex financial problems and generate actionable insights. Acting at the intersection of analytics, data and decision\-making, the role supports the development and evaluation of analytical approaches and contributes to the delivery of scalable Financial Analytics products. Across Finance the role contributes to advancing analytical capabilities, supports innovation and promotes effective use of data and AI.
Responsibilities
This role collaborates closely with Financial Analytics Product Managers, Data Engineers, and further functions to translate business challenges into analytical approaches and measurable outcomes. The role contributes across the analytical lifecycle by supporting data exploration, hypothesis development, model development, evaluation and interpretation of analytical results. To be successful in this role, it requires strong analytical thinking, curiosity and the ability to translate complex data into meaningful insights while balancing methodological rigor and business applicability.
The role focuses on the following key responsibility areas:
Translate business challenges into analytical questions, measurable outcomes and suitable analytical approaches
Perform exploratory data analysis, assess data suitability and develop hypotheses to guide analytical work
Select, develop, test and validate advanced analytics and AI models using appropriate statistical and machine learning techniques
Interpret analytical outputs, communicate assumptions and limitations transparently, and translate findings into actionable recommendations
Enable scalable analytics delivery by collaborating with Product Managers and Data Engineers, promoting reuse of analytical methods, and ensuring analytical quality, explainability and compliance with governance standards
Responsibilities Continued
Qualifications
Education Qualifications
Bachelor's Degree required Master's Degree data science, statistics, mathematics, computer science, economics, finance or a related field preferred
Experience Qualifications
5\+ years data science, advanced analytics, machine learning or AI, preferably in the finance domain required
Experience in applying statistical and analytical methods to solve business problems required
Experience in developing and evaluating analytical models and generating actionable insights required
Proven ability to work in cross\-functional delivery environments and collaborate across technical teams required
Understanding of model lifecycle concepts and analytical delivery approaches preferred
Experience in industries such as pharmaceuticals and chemicals preferred
Travel Requirements
of the time. Limited
Additional Information
Daiichi Sankyo, Inc. is an equal opportunity/affirmative action employer. Qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected veteran status, age, or any other characteristic protected by law.
Salary Range:
USD$127,280\.00 \- USD$190,920\.00
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Salary Context
This $127K-$190K range is above the median for Data Scientist roles in our dataset (median: $160K across 258 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Daiichi Sankyo, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills in Demand for This Role
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 789 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($159K) sits 18% below the category median. Disclosed range: $127K to $190K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Daiichi Sankyo AI Hiring
Daiichi Sankyo has 1 open AI role right now. They're hiring across Data Scientist. Based in Basking Ridge, NJ, US. Compensation range: $190K - $190K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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